{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 背景与目的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "   共享，通过让渡闲置资源的使用权，在有限增加边际成本的前提下，提高了资源利用效率。Airbnb数据集包含结构化的表格数据、非结构化的文本和地图数据。本文基于Airbnb数据集，通过统计分析、时间序列、相关性分析、中英文文本情感分析、数据可视化以及数据应用等多方法对数据集特征进行分析，并探析各特征与价格的关系。数据来源:https://tianchi.aliyun.com/competition/entrance/231715/introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-03T13:04:31.750927Z",
     "start_time": "2019-11-03T13:04:31.719700Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import pandas_profiling\n",
    "import warnings \n",
    "import my_functions as my\n",
    "import datetime\n",
    "warnings.filterwarnings('ignore')\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from matplotlib import rcParams\n",
    "sns.set_style(\"ticks\") # dark/white/darkgrid/whitegrid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## listing表的诊断与清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-03T13:04:31.985296Z",
     "start_time": "2019-11-03T13:04:31.750927Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 3641984.00 MB\n",
      "Memory usage after optimization is: 3156184.00 MB\n",
      "Decreased by 13.3%\n",
      "Memory usage of dataframe is 9700880.00 MB\n",
      "Memory usage after optimization is: 14435538.00 MB\n",
      "Decreased by -48.8%\n"
     ]
    }
   ],
   "source": [
    "df1=my.reduce_mem_usage(pd.read_csv(\"listings.csv\"))\n",
    "df2=my.reduce_mem_usage(pd.read_csv(\"reviews_detail.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44054</td>\n",
       "      <td>Modern and Comfortable Living in CBD</td>\n",
       "      <td>192875</td>\n",
       "      <td>East Apartments</td>\n",
       "      <td>NaN</td>\n",
       "      <td>朝阳区 / Chaoyang</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>792</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>0.850098</td>\n",
       "      <td>9</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100213</td>\n",
       "      <td>The Great Wall Box Deluxe Suite A团园长城小院东院套房</td>\n",
       "      <td>527062</td>\n",
       "      <td>Joe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>密云县 / Miyun</td>\n",
       "      <td>40.68750</td>\n",
       "      <td>117.1875</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1201</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128496</td>\n",
       "      <td>Heart of Beijing: House with View 2</td>\n",
       "      <td>467520</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>389</td>\n",
       "      <td>3</td>\n",
       "      <td>259</td>\n",
       "      <td>2019-02-05</td>\n",
       "      <td>2.699219</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>161902</td>\n",
       "      <td>cozy studio in center of Beijing</td>\n",
       "      <td>707535</td>\n",
       "      <td>Robert</td>\n",
       "      <td>NaN</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>376</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2016-12-03</td>\n",
       "      <td>0.280029</td>\n",
       "      <td>5</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162144</td>\n",
       "      <td>nice studio near subway, sleep 4</td>\n",
       "      <td>707535</td>\n",
       "      <td>Robert</td>\n",
       "      <td>NaN</td>\n",
       "      <td>朝阳区 / Chaoyang</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>537</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>0.399902</td>\n",
       "      <td>5</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                                         name  host_id  \\\n",
       "0   44054         Modern and Comfortable Living in CBD   192875   \n",
       "1  100213  The Great Wall Box Deluxe Suite A团园长城小院东院套房   527062   \n",
       "2  128496          Heart of Beijing: House with View 2   467520   \n",
       "3  161902             cozy studio in center of Beijing   707535   \n",
       "4  162144            nice studio near subway, sleep 4    707535   \n",
       "\n",
       "         host_name  neighbourhood_group   neighbourhood  latitude  longitude  \\\n",
       "0  East Apartments                  NaN  朝阳区 / Chaoyang  39.90625   116.4375   \n",
       "1              Joe                  NaN     密云县 / Miyun  40.68750   117.1875   \n",
       "2            Cindy                  NaN             东城区  39.93750   116.4375   \n",
       "3           Robert                  NaN             东城区  39.93750   116.4375   \n",
       "4           Robert                  NaN  朝阳区 / Chaoyang  39.93750   116.4375   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0  Entire home/apt    792               1                 89  2019-03-04   \n",
       "1     Private room   1201               1                  2  2017-10-08   \n",
       "2  Entire home/apt    389               3                259  2019-02-05   \n",
       "3  Entire home/apt    376               1                 26  2016-12-03   \n",
       "4  Entire home/apt    537               1                 37  2018-08-01   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0           0.850098                               9               341  \n",
       "1           0.099976                               4                 0  \n",
       "2           2.699219                               1                93  \n",
       "3           0.280029                               5               290  \n",
       "4           0.399902                               5               352  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head() #查看大体上是否不干净的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "name和neighbourhood中英文混合，需要进一步清洗。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28452 entries, 0 to 28451\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype   \n",
      "---  ------                          --------------  -----   \n",
      " 0   id                              28452 non-null  int32   \n",
      " 1   name                            28451 non-null  category\n",
      " 2   host_id                         28452 non-null  int32   \n",
      " 3   host_name                       28452 non-null  category\n",
      " 4   neighbourhood_group             0 non-null      float64 \n",
      " 5   neighbourhood                   28452 non-null  category\n",
      " 6   latitude                        28452 non-null  float16 \n",
      " 7   longitude                       28452 non-null  float16 \n",
      " 8   room_type                       28452 non-null  category\n",
      " 9   price                           28452 non-null  int32   \n",
      " 10  minimum_nights                  28452 non-null  int16   \n",
      " 11  number_of_reviews               28452 non-null  int16   \n",
      " 12  last_review                     17294 non-null  category\n",
      " 13  reviews_per_month               17294 non-null  float16 \n",
      " 14  calculated_host_listings_count  28452 non-null  int16   \n",
      " 15  availability_365                28452 non-null  int16   \n",
      "dtypes: category(5), float16(3), float64(1), int16(4), int32(3)\n",
      "memory usage: 3.0 MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>nullrate</th>\n",
       "      <th>nullrate%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <td>28452</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reviews_per_month</th>\n",
       "      <td>11158</td>\n",
       "      <td>0.392169</td>\n",
       "      <td>39.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_review</th>\n",
       "      <td>11158</td>\n",
       "      <td>0.392169</td>\n",
       "      <td>39.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0  nullrate  nullrate%\n",
       "neighbourhood_group  28452  1.000000     100.00\n",
       "reviews_per_month    11158  0.392169      39.22\n",
       "last_review          11158  0.392169      39.22\n",
       "name                     1  0.000035       0.00"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my.Nullrate(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "neighbourhood_group全为缺失值，name有一个缺失值，考虑移除。last_review和reviews_per_month拥有相同比例的缺失值，考虑缺失类型为非完全随机缺失，且缺失量较大，不能简单的删除或者特殊固定值填充，本文将使用可处理缺失值XGB模型对价格进行预测，因此暂不处理这些缺失值。id以及host_id应为分类型数据；last_review应为日期型数据,并继续钻取年、月、周等数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(\"neighbourhood_group\",axis=1,inplace=True) #移除neighbourhood_group\n",
    "df1.id=df1.id.astype(\"category\") #数据类型转换\n",
    "df1.host_id=df1.host_id.astype(\"category\") #数据类型转换\n",
    "df1.last_review=pd.to_datetime(df1.last_review) #数据类型转换\n",
    "df1[\"year\"]=df1.last_review.dt.year #日期数据钻取，以便时间序列分析\n",
    "df1[\"month\"]=df1.last_review.dt.month\n",
    "df1[\"weekday\"]=df1.last_review.dt.weekday #返回周几\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from chinese_calendar import is_workday, is_holiday,is_in_lieu  #判断日期是否工作日；节假日；调休\n",
    "#由于时间序列数据是德国的日历；因此不能用中国日历来创建特征。\n",
    "creat_time=pd.to_datetime(df1.last_review, errors='coerce')\n",
    "df1[\"is_workday\"]=creat_time.apply(lambda x:is_workday(x) )\n",
    "df1[\"is_holiday\"]=creat_time.apply(lambda x:is_holiday(x) )\n",
    "df1[\"is_in_lieu\"]=creat_time.apply(lambda x:is_in_lieu(x) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>minimum_nights</th>\n",
       "      <th>...</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
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       "      <th>0</th>\n",
       "      <td>44054</td>\n",
       "      <td>Modern and Comfortable Living in CBD</td>\n",
       "      <td>192875</td>\n",
       "      <td>East Apartments</td>\n",
       "      <td>朝阳区 / Chaoyang</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.4375</td>\n",
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       "      <td>1</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100213</td>\n",
       "      <td>The Great Wall Box Deluxe Suite A团园长城小院东院套房</td>\n",
       "      <td>527062</td>\n",
       "      <td>Joe</td>\n",
       "      <td>密云县 / Miyun</td>\n",
       "      <td>40.68750</td>\n",
       "      <td>117.1875</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1201</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128496</td>\n",
       "      <td>Heart of Beijing: House with View 2</td>\n",
       "      <td>467520</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>389</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-02-05</td>\n",
       "      <td>2.699219</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>161902</td>\n",
       "      <td>cozy studio in center of Beijing</td>\n",
       "      <td>707535</td>\n",
       "      <td>Robert</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>376</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-12-03</td>\n",
       "      <td>0.280029</td>\n",
       "      <td>5</td>\n",
       "      <td>290</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162144</td>\n",
       "      <td>nice studio near subway, sleep 4</td>\n",
       "      <td>707535</td>\n",
       "      <td>Robert</td>\n",
       "      <td>朝阳区 / Chaoyang</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>537</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>0.399902</td>\n",
       "      <td>5</td>\n",
       "      <td>352</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                                         name host_id  \\\n",
       "0   44054         Modern and Comfortable Living in CBD  192875   \n",
       "1  100213  The Great Wall Box Deluxe Suite A团园长城小院东院套房  527062   \n",
       "2  128496          Heart of Beijing: House with View 2  467520   \n",
       "3  161902             cozy studio in center of Beijing  707535   \n",
       "4  162144            nice studio near subway, sleep 4   707535   \n",
       "\n",
       "         host_name   neighbourhood  latitude  longitude        room_type  \\\n",
       "0  East Apartments  朝阳区 / Chaoyang  39.90625   116.4375  Entire home/apt   \n",
       "1              Joe     密云县 / Miyun  40.68750   117.1875     Private room   \n",
       "2            Cindy             东城区  39.93750   116.4375  Entire home/apt   \n",
       "3           Robert             东城区  39.93750   116.4375  Entire home/apt   \n",
       "4           Robert  朝阳区 / Chaoyang  39.93750   116.4375  Entire home/apt   \n",
       "\n",
       "   price  minimum_nights  ...  last_review reviews_per_month  \\\n",
       "0    792               1  ...   2019-03-04          0.850098   \n",
       "1   1201               1  ...   2017-10-08          0.099976   \n",
       "2    389               3  ...   2019-02-05          2.699219   \n",
       "3    376               1  ...   2016-12-03          0.280029   \n",
       "4    537               1  ...   2018-08-01          0.399902   \n",
       "\n",
       "   calculated_host_listings_count  availability_365    year  month  weekday  \\\n",
       "0                               9               341  2019.0    3.0      0.0   \n",
       "1                               4                 0  2017.0   10.0      6.0   \n",
       "2                               1                93  2019.0    2.0      1.0   \n",
       "3                               5               290  2016.0   12.0      5.0   \n",
       "4                               5               352  2018.0    8.0      2.0   \n",
       "\n",
       "   is_workday is_holiday is_in_lieu  \n",
       "0        True      False      False  \n",
       "1       False       True      False  \n",
       "2       False       True      False  \n",
       "3       False       True      False  \n",
       "4        True      False      False  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分类特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_workday</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_in_lieu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17294</td>\n",
       "      <td>17294</td>\n",
       "      <td>17294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>8877</td>\n",
       "      <td>8877</td>\n",
       "      <td>16788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       is_workday is_holiday is_in_lieu\n",
       "count       17294      17294      17294\n",
       "unique          2          2          2\n",
       "top         False       True      False\n",
       "freq         8877       8877      16788"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe(include=[\"O\"]) #查看分类型数据统计结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "id 是房源id，在整个记录中不重复，可作为主键进行索引。id与name数值不一致，说明name中存在重复描述，最大重复值为28，即一个name使用了28次，可能存在异常。\n",
    "host_id与host_name数值也不一致，说明host_name可能存在多个同名同姓。neighbourhood存在16个非重复字段，类型有点多，且结合上文的head（）结果，neighbourhood需要进一步清洗。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.neighbourhood.value_counts()\n",
    "df1=df1[df1.name.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据格式不统一，中英混合或纯中文，因此可以考虑滤除非汉字部分，统一数据格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[\"neighbourhood\"]=df1[\"neighbourhood\"].apply(lambda x:re.sub(\"[A-Za-z0-9/]\", \"\",x)) #滤除非汉字部分\n",
    "df1[\"neighbourhood\"]=df1[\"neighbourhood\"].apply(lambda x:re.sub(\"县\", \"区\",x)) #替换\n",
    "df1[\"neighbourhood\"]=df1[\"neighbourhood\"].apply(lambda x:re.sub(\" \", \"\",x)) #消除空格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#计数条形图，获取分类数据大致分布情况\n",
    "category_item=['room_type','neighbourhood']\n",
    "fig,axes=plt.subplots(1,len(category_item),figsize=(13,5))\n",
    "plt.subplots_adjust(hspace=1)\n",
    "plt.xticks(rotation=-90)\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "for i,subset in enumerate(category_item):\n",
    "    sns.countplot(x=df1[subset],ax=axes[i],color=\"c\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数值特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28451.00000</td>\n",
       "      <td>2.845100e+04</td>\n",
       "      <td>28451.000000</td>\n",
       "      <td>28451.000000</td>\n",
       "      <td>28451.000000</td>\n",
       "      <td>17294.000000</td>\n",
       "      <td>28451.000000</td>\n",
       "      <td>28451.000000</td>\n",
       "      <td>17294.000000</td>\n",
       "      <td>17294.000000</td>\n",
       "      <td>17294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>610.873396</td>\n",
       "      <td>2.729746</td>\n",
       "      <td>7.103406</td>\n",
       "      <td>1.320312</td>\n",
       "      <td>12.818706</td>\n",
       "      <td>220.337071</td>\n",
       "      <td>2018.589164</td>\n",
       "      <td>4.840985</td>\n",
       "      <td>3.527640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.18811</td>\n",
       "      <td>2.055664e-01</td>\n",
       "      <td>1622.609504</td>\n",
       "      <td>17.921244</td>\n",
       "      <td>16.815310</td>\n",
       "      <td>1.581055</td>\n",
       "      <td>29.261751</td>\n",
       "      <td>138.430490</td>\n",
       "      <td>0.650965</td>\n",
       "      <td>3.072522</td>\n",
       "      <td>2.163666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>39.46875</td>\n",
       "      <td>1.155000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5%</th>\n",
       "      <td>39.81250</td>\n",
       "      <td>1.161875e+02</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090027</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.90625</td>\n",
       "      <td>1.163750e+02</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.290039</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.93750</td>\n",
       "      <td>1.164375e+02</td>\n",
       "      <td>389.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.799805</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.00000</td>\n",
       "      <td>1.165000e+02</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>40.43750</td>\n",
       "      <td>1.166875e+02</td>\n",
       "      <td>1691.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>4.519531</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.93750</td>\n",
       "      <td>1.175000e+02</td>\n",
       "      <td>68983.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          latitude     longitude         price  minimum_nights  \\\n",
       "count  28451.00000  2.845100e+04  28451.000000    28451.000000   \n",
       "mean           inf           inf    610.873396        2.729746   \n",
       "std        0.18811  2.055664e-01   1622.609504       17.921244   \n",
       "min       39.46875  1.155000e+02      0.000000        1.000000   \n",
       "5%        39.81250  1.161875e+02    107.000000        1.000000   \n",
       "25%       39.90625  1.163750e+02    235.000000        1.000000   \n",
       "50%       39.93750  1.164375e+02    389.000000        1.000000   \n",
       "75%       40.00000  1.165000e+02    577.000000        1.000000   \n",
       "95%       40.43750  1.166875e+02   1691.000000        5.000000   \n",
       "max       40.93750  1.175000e+02  68983.000000     1125.000000   \n",
       "\n",
       "       number_of_reviews  reviews_per_month  calculated_host_listings_count  \\\n",
       "count       28451.000000       17294.000000                    28451.000000   \n",
       "mean            7.103406           1.320312                       12.818706   \n",
       "std            16.815310           1.581055                       29.261751   \n",
       "min             0.000000           0.010002                        1.000000   \n",
       "5%              0.000000           0.090027                        1.000000   \n",
       "25%             0.000000           0.290039                        2.000000   \n",
       "50%             1.000000           0.799805                        5.000000   \n",
       "75%             6.000000           1.750000                       11.000000   \n",
       "95%            35.000000           4.519531                       45.000000   \n",
       "max           322.000000          20.000000                      222.000000   \n",
       "\n",
       "       availability_365          year         month       weekday  \n",
       "count      28451.000000  17294.000000  17294.000000  17294.000000  \n",
       "mean         220.337071   2018.589164      4.840985      3.527640  \n",
       "std          138.430490      0.650965      3.072522      2.163666  \n",
       "min            0.000000   2012.000000      1.000000      0.000000  \n",
       "5%             0.000000   2017.000000      1.000000      0.000000  \n",
       "25%           87.000000   2018.000000      3.000000      2.000000  \n",
       "50%          209.000000   2019.000000      4.000000      4.000000  \n",
       "75%          361.000000   2019.000000      7.000000      6.000000  \n",
       "95%          365.000000   2019.000000     11.000000      6.000000  \n",
       "max          365.000000   2019.000000     12.000000      6.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe(percentiles=[0.05,0.25,0.5,0.75,0.95]) #数值型数据统计结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#箱型图，获取数据大致分布情况，查看离群点信息\n",
    "subsets=['price','minimum_nights','number_of_reviews','reviews_per_month','calculated_host_listings_count','availability_365']\n",
    "fig,axes=plt.subplots(len(subsets),1,figsize=(13,9))\n",
    "plt.subplots_adjust(hspace=1)\n",
    "for i,subset in enumerate(subsets):\n",
    "    sns.boxplot(df1[subset],ax=axes[i],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#直方图，获取数据大致分布情况，查看集中信息\n",
    "fig2,[[ax1,ax2,ax3],[ax4,ax5,ax6]]=plt.subplots(2,3,figsize=(13,7))\n",
    "axes2=[ax1,ax2,ax3,ax4,ax5,ax6]\n",
    "plt.subplots_adjust(hspace=.3, wspace=.4)\n",
    "data=df1[(df1.price<=1778) & (df1.minimum_nights<=5) & (df1.number_of_reviews<=50)]\n",
    "data=data.dropna(how=\"any\")\n",
    "for i,subset in enumerate(subsets):\n",
    "    sns.distplot(data[subset],ax=axes2[i]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "price为0的记录考虑删除，价格极差很大，标准差系数达到2.65，对于高于10000的记录需要进一步诊断是否异常，价格很高有可能房源是别墅等豪宅。\n",
    "minimum_nights：95%在5以内，99%在30以内，最大值达到1125，意味着这个房源至少需要连续订阅1125个晚上，与常理不是很吻合,需要进一步诊断。\n",
    "number_of_reviews:极差很大，99%位数与max差异也很大，需要查看是否存在异常。\n",
    "availability_365：应在1~365之间，在此区间外的数据应该删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>...</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>is_workday</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_in_lieu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>12689987</td>\n",
       "      <td>Artistic apartment with culture</td>\n",
       "      <td>68973377</td>\n",
       "      <td>晨斌</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.5625</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>67104</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>0.059998</td>\n",
       "      <td>2</td>\n",
       "      <td>365</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>13418237</td>\n",
       "      <td>Great Wall Paradise Villa</td>\n",
       "      <td>6628610</td>\n",
       "      <td>Chris</td>\n",
       "      <td>怀柔区</td>\n",
       "      <td>40.46875</td>\n",
       "      <td>116.6250</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10066</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>0.189941</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>15488817</td>\n",
       "      <td>Hotel apartment close to huge Mall</td>\n",
       "      <td>68973377</td>\n",
       "      <td>晨斌</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.5625</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>63346</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-04-17</td>\n",
       "      <td>0.549805</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3427</th>\n",
       "      <td>18554424</td>\n",
       "      <td>东五环北欧风小两居</td>\n",
       "      <td>46923669</td>\n",
       "      <td>Shirley</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.84375</td>\n",
       "      <td>116.5625</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10998</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4199</th>\n",
       "      <td>19643684</td>\n",
       "      <td>观城9号(整院儿   圣泉寺 慕田峪 箭扣长城 红螺寺 响水湖)</td>\n",
       "      <td>47672470</td>\n",
       "      <td>经纬</td>\n",
       "      <td>顺义区</td>\n",
       "      <td>40.18750</td>\n",
       "      <td>116.8750</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>33471</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                                name   host_id host_name  \\\n",
       "1067  12689987     Artistic apartment with culture  68973377        晨斌   \n",
       "1227  13418237           Great Wall Paradise Villa   6628610     Chris   \n",
       "2012  15488817  Hotel apartment close to huge Mall  68973377        晨斌   \n",
       "3427  18554424                           东五环北欧风小两居  46923669   Shirley   \n",
       "4199  19643684    观城9号(整院儿   圣泉寺 慕田峪 箭扣长城 红螺寺 响水湖)  47672470        经纬   \n",
       "\n",
       "     neighbourhood  latitude  longitude        room_type  price  \\\n",
       "1067           朝阳区  39.93750   116.5625  Entire home/apt  67104   \n",
       "1227           怀柔区  40.46875   116.6250  Entire home/apt  10066   \n",
       "2012           朝阳区  39.90625   116.5625  Entire home/apt  63346   \n",
       "3427           朝阳区  39.84375   116.5625  Entire home/apt  10998   \n",
       "4199           顺义区  40.18750   116.8750  Entire home/apt  33471   \n",
       "\n",
       "      minimum_nights  ...  last_review reviews_per_month  \\\n",
       "1067               1  ...   2016-06-03          0.059998   \n",
       "1227               1  ...   2019-04-06          0.189941   \n",
       "2012               1  ...   2017-04-17          0.549805   \n",
       "3427               1  ...          NaT               NaN   \n",
       "4199               1  ...          NaT               NaN   \n",
       "\n",
       "      calculated_host_listings_count  availability_365    year  month  \\\n",
       "1067                               2               365  2016.0    6.0   \n",
       "1227                               1               364  2019.0    4.0   \n",
       "2012                               2               180  2017.0    4.0   \n",
       "3427                               2               364     NaN    NaN   \n",
       "4199                               6               360     NaN    NaN   \n",
       "\n",
       "      weekday  is_workday is_holiday is_in_lieu  \n",
       "1067      4.0        True      False      False  \n",
       "1227      5.0       False       True      False  \n",
       "2012      0.0        True      False      False  \n",
       "3427      NaN         NaN        NaN        NaN  \n",
       "4199      NaN         NaN        NaN        NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1.price>=10000].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entire home/apt    33\n",
       "Private room       11\n",
       "Shared room         4\n",
       "Name: room_type, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1.price>=10000].room_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在超过10000的高价房源中，有4个房源为合租类型，与现实场景不太吻合，因此考虑将此记录移除。同时，对高价房源中房屋类型为单租且缺失值较多的记录也予以删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>...</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>is_workday</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_in_lieu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>13183350</td>\n",
       "      <td>鸟巢旁欧式罗曼蒂克两居</td>\n",
       "      <td>56183837</td>\n",
       "      <td>Jack</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>40.00000</td>\n",
       "      <td>116.3750</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>463</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>0.059998</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>15948304</td>\n",
       "      <td>离车公庄地铁5分钟,离新街口步行十分钟｡交通便利｡环境幽雅,安全｡</td>\n",
       "      <td>78186433</td>\n",
       "      <td>Bo</td>\n",
       "      <td>西城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.3750</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>376</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3227</th>\n",
       "      <td>18258080</td>\n",
       "      <td>海淀魏公村万寿寺甲3号 3室1厅 160平米(只接受个人年长租)</td>\n",
       "      <td>122796150</td>\n",
       "      <td>Xu</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.3125</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>550</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3313</th>\n",
       "      <td>18402988</td>\n",
       "      <td>Beijing Sunshine place</td>\n",
       "      <td>96817654</td>\n",
       "      <td>宁Ning</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>396</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>1.179688</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4773</th>\n",
       "      <td>20311554</td>\n",
       "      <td>京都爱巢,为您准备</td>\n",
       "      <td>144803735</td>\n",
       "      <td>福春</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>39.96875</td>\n",
       "      <td>116.3125</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>490</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4836</th>\n",
       "      <td>20417131</td>\n",
       "      <td>护城河边,观望京都</td>\n",
       "      <td>144803735</td>\n",
       "      <td>福春</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.3125</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>591</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>20589657</td>\n",
       "      <td>爱屋吉屋</td>\n",
       "      <td>130855713</td>\n",
       "      <td>茜</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.84375</td>\n",
       "      <td>116.5625</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>302</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5609</th>\n",
       "      <td>21098578</td>\n",
       "      <td>6号14号线金台路青旅燕儿窝上下铺国贸CBD</td>\n",
       "      <td>129486966</td>\n",
       "      <td>磊</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.5000</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>121</td>\n",
       "      <td>1124</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>0.330078</td>\n",
       "      <td>6</td>\n",
       "      <td>362</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5857</th>\n",
       "      <td>21319861</td>\n",
       "      <td>嵩山居·首都机场店</td>\n",
       "      <td>154311879</td>\n",
       "      <td>涛</td>\n",
       "      <td>昌平区</td>\n",
       "      <td>40.09375</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Private room</td>\n",
       "      <td>268</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6505</th>\n",
       "      <td>21841908</td>\n",
       "      <td>鸟巢水立方朝南大主卧</td>\n",
       "      <td>159278266</td>\n",
       "      <td>小梦</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>40.03125</td>\n",
       "      <td>116.3750</td>\n",
       "      <td>Private room</td>\n",
       "      <td>255</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                               name    host_id host_name  \\\n",
       "1175  13183350                        鸟巢旁欧式罗曼蒂克两居   56183837      Jack   \n",
       "2192  15948304  离车公庄地铁5分钟,离新街口步行十分钟｡交通便利｡环境幽雅,安全｡   78186433        Bo   \n",
       "3227  18258080   海淀魏公村万寿寺甲3号 3室1厅 160平米(只接受个人年长租)  122796150        Xu   \n",
       "3313  18402988             Beijing Sunshine place   96817654     宁Ning   \n",
       "4773  20311554                          京都爱巢,为您准备  144803735        福春   \n",
       "4836  20417131                          护城河边,观望京都  144803735        福春   \n",
       "4981  20589657                               爱屋吉屋  130855713         茜   \n",
       "5609  21098578             6号14号线金台路青旅燕儿窝上下铺国贸CBD  129486966         磊   \n",
       "5857  21319861                          嵩山居·首都机场店  154311879         涛   \n",
       "6505  21841908                         鸟巢水立方朝南大主卧  159278266        小梦   \n",
       "\n",
       "     neighbourhood  latitude  longitude        room_type  price  \\\n",
       "1175           朝阳区  40.00000   116.3750  Entire home/apt    463   \n",
       "2192           西城区  39.93750   116.3750  Entire home/apt    376   \n",
       "3227           海淀区  39.93750   116.3125  Entire home/apt    550   \n",
       "3313           东城区  39.90625   116.4375  Entire home/apt    396   \n",
       "4773           海淀区  39.96875   116.3125  Entire home/apt    490   \n",
       "4836           海淀区  39.90625   116.3125  Entire home/apt    591   \n",
       "4981           朝阳区  39.84375   116.5625  Entire home/apt    302   \n",
       "5609           朝阳区  39.90625   116.5000      Shared room    121   \n",
       "5857           昌平区  40.09375   116.4375     Private room    268   \n",
       "6505           海淀区  40.03125   116.3750     Private room    255   \n",
       "\n",
       "      minimum_nights  ...  last_review reviews_per_month  \\\n",
       "1175            1000  ...   2016-07-25          0.059998   \n",
       "2192             365  ...          NaT               NaN   \n",
       "3227             365  ...          NaT               NaN   \n",
       "3313             365  ...   2018-09-30          1.179688   \n",
       "4773             365  ...   2017-08-20          0.099976   \n",
       "4836             365  ...          NaT               NaN   \n",
       "4981             365  ...          NaT               NaN   \n",
       "5609            1124  ...   2018-08-03          0.330078   \n",
       "5857             365  ...          NaT               NaN   \n",
       "6505            1000  ...          NaT               NaN   \n",
       "\n",
       "      calculated_host_listings_count  availability_365    year  month  \\\n",
       "1175                               1                 0  2016.0    7.0   \n",
       "2192                               1               364     NaN    NaN   \n",
       "3227                               1               365     NaN    NaN   \n",
       "3313                               1               364  2018.0    9.0   \n",
       "4773                               2                90  2017.0    8.0   \n",
       "4836                               2               365     NaN    NaN   \n",
       "4981                               1               362     NaN    NaN   \n",
       "5609                               6               362  2018.0    8.0   \n",
       "5857                               1               364     NaN    NaN   \n",
       "6505                               1                 0     NaN    NaN   \n",
       "\n",
       "      weekday  is_workday is_holiday is_in_lieu  \n",
       "1175      0.0        True      False      False  \n",
       "2192      NaN         NaN        NaN        NaN  \n",
       "3227      NaN         NaN        NaN        NaN  \n",
       "3313      6.0        True      False      False  \n",
       "4773      6.0       False       True      False  \n",
       "4836      NaN         NaN        NaN        NaN  \n",
       "4981      NaN         NaN        NaN        NaN  \n",
       "5609      4.0        True      False      False  \n",
       "5857      NaN         NaN        NaN        NaN  \n",
       "6505      NaN         NaN        NaN        NaN  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1.minimum_nights>=365].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：有些房源以年来长租，因此可能存在minimum_nights大于365的情况，不是异常，例如：id位18258080的房源。但如果房子年租（minimum_nights>365），且price超过95%分位数则可能是异常数据，考虑移除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df1[~((df1.price>10000)&(df1.room_type.isin([\"Private room\",\"Shared room\"]))&(df1.last_review.isnull()))]\n",
    "df1=df1[~((df1.minimum_nights>365)&(df1.price>df1.price.quantile(q=0.95)))]\n",
    "df1=df1[df1[\"price\"]>0]\n",
    "df1=df1[(0<df1[\"availability_365\"]) & (df1[\"availability_365\"]<=365) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## review表的诊断与清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44054</td>\n",
       "      <td>84748</td>\n",
       "      <td>2010-08-25</td>\n",
       "      <td>207019</td>\n",
       "      <td>Jarrod</td>\n",
       "      <td>Sev was very helpful.  Sev showed us where to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44054</td>\n",
       "      <td>118384</td>\n",
       "      <td>2010-10-13</td>\n",
       "      <td>218723</td>\n",
       "      <td>Kimberly</td>\n",
       "      <td>We arrived in Beijing very early in the mornin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44054</td>\n",
       "      <td>436978</td>\n",
       "      <td>2011-08-11</td>\n",
       "      <td>609177</td>\n",
       "      <td>Emma</td>\n",
       "      <td>It is a really massive apartment and really co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202096</th>\n",
       "      <td>33891613</td>\n",
       "      <td>438182693</td>\n",
       "      <td>2019-04-16</td>\n",
       "      <td>255994654</td>\n",
       "      <td>天佑</td>\n",
       "      <td>房间布置明亮温暖而别有风格,指引周到而迅速,房间内设施齐全､空间宽敞!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202097</th>\n",
       "      <td>33892088</td>\n",
       "      <td>438119657</td>\n",
       "      <td>2019-04-16</td>\n",
       "      <td>255993753</td>\n",
       "      <td>志强</td>\n",
       "      <td>各方面这个价位性价比是非常之高了,房东人也很好,交通便利,我个人是很满意的,有机会还会来｡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202098</th>\n",
       "      <td>33925874</td>\n",
       "      <td>438572523</td>\n",
       "      <td>2019-04-17</td>\n",
       "      <td>256375057</td>\n",
       "      <td>玲</td>\n",
       "      <td>The host canceled this reservation 13 days bef...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        listing_id         id        date  reviewer_id reviewer_name  \\\n",
       "0            44054      84748  2010-08-25       207019        Jarrod   \n",
       "1            44054     118384  2010-10-13       218723      Kimberly   \n",
       "2            44054     436978  2011-08-11       609177          Emma   \n",
       "202096    33891613  438182693  2019-04-16    255994654            天佑   \n",
       "202097    33892088  438119657  2019-04-16    255993753            志强   \n",
       "202098    33925874  438572523  2019-04-17    256375057             玲   \n",
       "\n",
       "                                                 comments  \n",
       "0       Sev was very helpful.  Sev showed us where to ...  \n",
       "1       We arrived in Beijing very early in the mornin...  \n",
       "2       It is a really massive apartment and really co...  \n",
       "202096                房间布置明亮温暖而别有风格,指引周到而迅速,房间内设施齐全､空间宽敞!  \n",
       "202097      各方面这个价位性价比是非常之高了,房东人也很好,交通便利,我个人是很满意的,有机会还会来｡  \n",
       "202098  The host canceled this reservation 13 days bef...  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(3).append(df2.tail(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 202099 entries, 0 to 202098\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count   Dtype   \n",
      "---  ------         --------------   -----   \n",
      " 0   listing_id     202099 non-null  int32   \n",
      " 1   id             202099 non-null  int32   \n",
      " 2   date           202099 non-null  category\n",
      " 3   reviewer_id    202099 non-null  int32   \n",
      " 4   reviewer_name  202093 non-null  category\n",
      " 5   comments       201983 non-null  category\n",
      "dtypes: category(3), int32(3)\n",
      "memory usage: 13.8 MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data_review异常诊断：\n",
    "reviewer_name和comments 存在一定程度缺失，由于确实比例不是很大，考虑直接移除缺失记录。\n",
    "date应为日期格式；id、listing_id、reviewer_id应为类别型数据；reviewer_name中英混合，可以想办法进行整合。\n",
    "comments中英混合，考虑进行拆分，进而做中英文文本情感分析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df2.dropna(axis=0) #移除有缺失值的行\n",
    "df2.id=df2.id.astype('object')\n",
    "df2.reviewer_id=df2.reviewer_id.astype(\"object\")\n",
    "df2.listing_id=df2.listing_id.astype(\"object\")\n",
    "df2.date=pd.to_datetime(df2.date)\n",
    "df2[\"Chinese_comments\"]=df2.comments.apply(lambda x:re.sub(\"[A-Za-z0-9\\!\\%\\?()./[\\]\\- \t:;,\\。 ' ...r'\\n\\r\\n\\r\\n'\"\"\t \t]\", \"\",str(x))) #滤除非汉字部分\n",
    "df2[\"Chinese_comments\"]=df2[\"Chinese_comments\"].apply(lambda x : re.sub(\"\",\"无\",x))\n",
    "df2[\"English_comments\"]=df2.comments.apply(lambda x:re.sub(\"[\\u4e00-\\u9fa5...r'\\n\\r\\n\\r\\n'!\"\"\t]\", \"\",str(x))) #滤除汉字部分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NLP情感倾向统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob #英文文本情感分析库\n",
    "def CalPolarity(text):\n",
    "    blob=TextBlob(text)\n",
    "    return blob.sentiment.polarity #输出该文本的情感极数（-1~1）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2[\"Polarity\"]=df2.English_comments.apply(lambda x: CalPolarity(x) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from snownlp import SnowNLP #中文文本情感分析库\n",
    "def CalProbability(text): \n",
    "    s = SnowNLP(text)\n",
    "    return s.sentiments #输出该文本为正面评价的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2.Chinese_comments=df2.Chinese_comments.apply(lambda x: re.sub(r\"\", \"无\", str(x))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2[\"ProbaPositive\"]=df2.Chinese_comments.apply(lambda x: CalProbability(x) ) #运行时间太长；将线下运行的结果import进来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.read_csv(\"df2new_pro.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf2=df2.groupby(\"listing_id\")[\"Polarity\",\"ProbaPositive\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Polarity</th>\n",
       "      <th>ProbaPositive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listing_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44054</th>\n",
       "      <td>0.205774</td>\n",
       "      <td>0.511484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100213</th>\n",
       "      <td>0.202778</td>\n",
       "      <td>0.557248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128496</th>\n",
       "      <td>0.368798</td>\n",
       "      <td>0.424662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161902</th>\n",
       "      <td>0.269895</td>\n",
       "      <td>0.415101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162144</th>\n",
       "      <td>0.204663</td>\n",
       "      <td>0.491734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Polarity  ProbaPositive\n",
       "listing_id                         \n",
       "44054       0.205774       0.511484\n",
       "100213      0.202778       0.557248\n",
       "128496      0.368798       0.424662\n",
       "161902      0.269895       0.415101\n",
       "162144      0.204663       0.491734"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf=pd.merge(df1,newdf2,how=\"left\",left_on=\"id\",right_on=\"listing_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_feat(df,cols): #统计自身的各值频数\n",
    "    for col in cols:\n",
    "        newF1 = df.groupby([col])[col].count().to_frame().rename(columns={col:col+'__count'})\n",
    "        df = pd.merge(df, newF1, on=[col], how='left')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProbaPositive</th>\n",
       "      <th>Polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15800.000000</td>\n",
       "      <td>15800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.782865</td>\n",
       "      <td>0.030013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.207672</td>\n",
       "      <td>0.075553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.703401</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.826099</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.941616</td>\n",
       "      <td>0.027852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProbaPositive      Polarity\n",
       "count   15800.000000  15800.000000\n",
       "mean        0.782865      0.030013\n",
       "std         0.207672      0.075553\n",
       "min         0.000000     -0.533333\n",
       "25%         0.703401      0.000000\n",
       "50%         0.826099      0.000000\n",
       "75%         0.941616      0.027852\n",
       "max         1.000000      1.000000"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf[[\"ProbaPositive\",'Polarity']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf=count_feat(newdf,[\"room_type\",\"neighbourhood\",\"host_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf[\"today\"]=datetime.date.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf[\"last_rewtonow\"]=pd.to_datetime(newdf[\"today\"])-pd.to_datetime(df1.last_review, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 25815 entries, 0 to 25814\n",
      "Data columns (total 28 columns):\n",
      " #   Column                          Non-Null Count  Dtype          \n",
      "---  ------                          --------------  -----          \n",
      " 0   id                              25815 non-null  int64          \n",
      " 1   name                            25815 non-null  category       \n",
      " 2   host_id                         25815 non-null  category       \n",
      " 3   host_name                       25815 non-null  category       \n",
      " 4   neighbourhood                   25815 non-null  category       \n",
      " 5   latitude                        25815 non-null  float16        \n",
      " 6   longitude                       25815 non-null  float16        \n",
      " 7   room_type                       25815 non-null  category       \n",
      " 8   price                           25815 non-null  int32          \n",
      " 9   minimum_nights                  25815 non-null  int16          \n",
      " 10  number_of_reviews               25815 non-null  int16          \n",
      " 11  last_review                     15804 non-null  category       \n",
      " 12  reviews_per_month               15804 non-null  float16        \n",
      " 13  calculated_host_listings_count  25815 non-null  int16          \n",
      " 14  availability_365                25815 non-null  int16          \n",
      " 15  year                            15804 non-null  float64        \n",
      " 16  month                           15804 non-null  float64        \n",
      " 17  weekday                         15804 non-null  float64        \n",
      " 18  is_workday                      15804 non-null  object         \n",
      " 19  is_holiday                      15804 non-null  object         \n",
      " 20  is_in_lieu                      15804 non-null  object         \n",
      " 21  Polarity                        15800 non-null  float64        \n",
      " 22  ProbaPositive                   15800 non-null  float64        \n",
      " 23  room_type__count                25815 non-null  int64          \n",
      " 24  neighbourhood__count            25815 non-null  int64          \n",
      " 25  host_id__count                  25815 non-null  int64          \n",
      " 26  today                           25815 non-null  object         \n",
      " 27  last_rewtonow                   15304 non-null  timedelta64[ns]\n",
      "dtypes: category(6), float16(3), float64(5), int16(4), int32(1), int64(4), object(4), timedelta64[ns](1)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "newdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_review</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>is_workday</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_in_lieu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-05</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-03</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-02</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  last_review    year  month  weekday is_workday is_holiday is_in_lieu\n",
       "0  2019-03-04  2019.0    3.0      0.0       True      False      False\n",
       "1  2019-02-05  2019.0    2.0      1.0      False       True      False\n",
       "2  2016-12-03  2016.0   12.0      5.0      False       True      False\n",
       "3  2018-08-01  2018.0    8.0      2.0       True      False      False\n",
       "4  2018-11-02  2018.0   11.0      4.0       True      False      False"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf[[\"last_review\",\"year\",\"month\",\"weekday\",\"is_workday\",\"is_holiday\",\"is_in_lieu\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf.to_csv(\"data_all\",index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多维度数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_feat=['price',\n",
    " 'minimum_nights',\n",
    " 'number_of_reviews',\n",
    " 'reviews_per_month',\n",
    " 'calculated_host_listings_count',\n",
    " 'availability_365',\n",
    " 'Polarity',\n",
    " 'ProbaPositive',\n",
    " 'room_type__count',\n",
    " 'neighbourhood__count',\n",
    " 'host_id__count',\n",
    " 'last_rewtonow']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 数值标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对参与计算的数值进行标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "for i in num_feat:\n",
    "    newdf[i] = ss.fit_transform(newdf[i].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>...</th>\n",
       "      <th>is_workday</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_in_lieu</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>ProbaPositive</th>\n",
       "      <th>room_type__count</th>\n",
       "      <th>neighbourhood__count</th>\n",
       "      <th>host_id__count</th>\n",
       "      <th>today</th>\n",
       "      <th>last_rewtonow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44054</td>\n",
       "      <td>Modern and Comfortable Living in CBD</td>\n",
       "      <td>192875</td>\n",
       "      <td>East Apartments</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>0.132517</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.326400</td>\n",
       "      <td>-1.306818</td>\n",
       "      <td>0.743393</td>\n",
       "      <td>1.269714</td>\n",
       "      <td>-0.141694</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.827420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128496</td>\n",
       "      <td>Heart of Beijing: House with View 2</td>\n",
       "      <td>467520</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.158064</td>\n",
       "      <td>0.034476</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4.484224</td>\n",
       "      <td>-1.724907</td>\n",
       "      <td>0.743393</td>\n",
       "      <td>-0.422563</td>\n",
       "      <td>-0.445191</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>-1.206644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161902</td>\n",
       "      <td>cozy studio in center of Beijing</td>\n",
       "      <td>707535</td>\n",
       "      <td>Robert</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.167438</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.175118</td>\n",
       "      <td>-1.770944</td>\n",
       "      <td>0.743393</td>\n",
       "      <td>-0.422563</td>\n",
       "      <td>-0.293442</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.827932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162144</td>\n",
       "      <td>nice studio near subway, sleep 4</td>\n",
       "      <td>707535</td>\n",
       "      <td>Robert</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.051350</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.311697</td>\n",
       "      <td>-1.401925</td>\n",
       "      <td>0.743393</td>\n",
       "      <td>1.269714</td>\n",
       "      <td>-0.293442</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.843000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>279078</td>\n",
       "      <td>Nice Apartment in Beijing</td>\n",
       "      <td>1455726</td>\n",
       "      <td>Fiona</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.147970</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.757127</td>\n",
       "      <td>-1.285243</td>\n",
       "      <td>0.743393</td>\n",
       "      <td>-0.422563</td>\n",
       "      <td>-0.217568</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.831500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                                  name  host_id        host_name  \\\n",
       "0   44054  Modern and Comfortable Living in CBD   192875  East Apartments   \n",
       "1  128496   Heart of Beijing: House with View 2   467520            Cindy   \n",
       "2  161902      cozy studio in center of Beijing   707535           Robert   \n",
       "3  162144     nice studio near subway, sleep 4    707535           Robert   \n",
       "4  279078             Nice Apartment in Beijing  1455726            Fiona   \n",
       "\n",
       "  neighbourhood  latitude  longitude        room_type     price  \\\n",
       "0           朝阳区  39.90625   116.4375  Entire home/apt  0.132517   \n",
       "1           东城区  39.93750   116.4375  Entire home/apt -0.158064   \n",
       "2           东城区  39.93750   116.4375  Entire home/apt -0.167438   \n",
       "3           朝阳区  39.93750   116.4375  Entire home/apt -0.051350   \n",
       "4           东城区  39.93750   116.4375  Entire home/apt -0.147970   \n",
       "\n",
       "   minimum_nights  ...  is_workday is_holiday  is_in_lieu  Polarity  \\\n",
       "0       -0.107866  ...        True      False       False  2.326400   \n",
       "1        0.034476  ...       False       True       False  4.484224   \n",
       "2       -0.107866  ...       False       True       False  3.175118   \n",
       "3       -0.107866  ...        True      False       False  2.311697   \n",
       "4       -0.107866  ...        True      False       False  1.757127   \n",
       "\n",
       "   ProbaPositive  room_type__count  neighbourhood__count  host_id__count  \\\n",
       "0      -1.306818          0.743393              1.269714       -0.141694   \n",
       "1      -1.724907          0.743393             -0.422563       -0.445191   \n",
       "2      -1.770944          0.743393             -0.422563       -0.293442   \n",
       "3      -1.401925          0.743393              1.269714       -0.293442   \n",
       "4      -1.285243          0.743393             -0.422563       -0.217568   \n",
       "\n",
       "        today last_rewtonow  \n",
       "0  2020-05-15      0.827420  \n",
       "1  2020-05-15     -1.206644  \n",
       "2  2020-05-15      0.827932  \n",
       "3  2020-05-15      0.843000  \n",
       "4  2020-05-15      0.831500  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 指标建立"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "#便捷度:minimum_nights+availability_365 +neighbourhood__count+room_type__count\n",
    "newdf[\"convenience\"]=0.35*(1-newdf.minimum_nights)+0.2*newdf.availability_365 +0.25*newdf.neighbourhood__count+0.2*newdf.room_type__count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "#热度：neighbourhood__count+number_of_reviews+reviews_per_month+last_rewtonow\n",
    "newdf[\"hotness\"]=0.15*newdf.neighbourhood__count+0.4*newdf.number_of_reviews+0.3*newdf.reviews_per_month+0.15*newdf.last_rewtonow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "#好评率：Polarity+ProbaPositive\n",
    "newdf[\"reputation\"]=0.4*newdf.Polarity+0.6*newdf.ProbaPositive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "#体验：便捷度+好评率\n",
    "newdf[\"experience\"]=0.3*newdf[\"convenience\"]+0.7*newdf[\"reputation\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "#性价比：体验/价格\n",
    "newdf[\"cost_performance\"]=newdf[\"experience\"]/newdf.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "#推荐度：体验+性价比+热度\n",
    "newdf[\"recommendation\"]=0.3*newdf[\"experience\"]+0.5*newdf[\"cost_performance\"]+0.2*newdf[\"hotness\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "#房东质量分数：房源量+房源推荐度\n",
    "newdf[\"host_quanlity\"]=0.6*newdf.host_id__count+0.4*newdf.recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>...</th>\n",
       "      <th>host_id__count</th>\n",
       "      <th>today</th>\n",
       "      <th>last_rewtonow</th>\n",
       "      <th>convenience</th>\n",
       "      <th>hotness</th>\n",
       "      <th>reputation</th>\n",
       "      <th>experience</th>\n",
       "      <th>cost_performance</th>\n",
       "      <th>recommendation</th>\n",
       "      <th>host_quanlity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44054</td>\n",
       "      <td>Modern and Comfortable Living in CBD</td>\n",
       "      <td>192875</td>\n",
       "      <td>East Apartments</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>0.132517</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.141694</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.827420</td>\n",
       "      <td>1.010856</td>\n",
       "      <td>2.113001</td>\n",
       "      <td>0.146469</td>\n",
       "      <td>0.405785</td>\n",
       "      <td>3.062132</td>\n",
       "      <td>2.075402</td>\n",
       "      <td>0.745144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128496</td>\n",
       "      <td>Heart of Beijing: House with View 2</td>\n",
       "      <td>467520</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.158064</td>\n",
       "      <td>0.034476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.445191</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>-1.206644</td>\n",
       "      <td>0.141861</td>\n",
       "      <td>5.847522</td>\n",
       "      <td>0.758745</td>\n",
       "      <td>0.573680</td>\n",
       "      <td>-3.629407</td>\n",
       "      <td>-0.473095</td>\n",
       "      <td>-0.456353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161902</td>\n",
       "      <td>cozy studio in center of Beijing</td>\n",
       "      <td>707535</td>\n",
       "      <td>Robert</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.167438</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.293442</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.827932</td>\n",
       "      <td>0.506329</td>\n",
       "      <td>0.289447</td>\n",
       "      <td>0.207481</td>\n",
       "      <td>0.297135</td>\n",
       "      <td>-1.774598</td>\n",
       "      <td>-0.740269</td>\n",
       "      <td>-0.472173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162144</td>\n",
       "      <td>nice studio near subway, sleep 4</td>\n",
       "      <td>707535</td>\n",
       "      <td>Robert</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.051350</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.293442</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.843000</td>\n",
       "      <td>1.028425</td>\n",
       "      <td>0.823387</td>\n",
       "      <td>0.083524</td>\n",
       "      <td>0.366994</td>\n",
       "      <td>-7.146972</td>\n",
       "      <td>-3.298710</td>\n",
       "      <td>-1.495550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>279078</td>\n",
       "      <td>Nice Apartment in Beijing</td>\n",
       "      <td>1455726</td>\n",
       "      <td>Fiona</td>\n",
       "      <td>东城区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.147970</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.217568</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.831500</td>\n",
       "      <td>0.606953</td>\n",
       "      <td>0.369097</td>\n",
       "      <td>-0.068295</td>\n",
       "      <td>0.134279</td>\n",
       "      <td>-0.907477</td>\n",
       "      <td>-0.339635</td>\n",
       "      <td>-0.266395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25810</th>\n",
       "      <td>33948728</td>\n",
       "      <td>望京西门子,798,央美附近 温馨如家大床民宿</td>\n",
       "      <td>256112655</td>\n",
       "      <td>旭</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>40.00000</td>\n",
       "      <td>116.5000</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.153017</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.445191</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>-1.206644</td>\n",
       "      <td>0.608361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25811</th>\n",
       "      <td>33948787</td>\n",
       "      <td>04简约舒适电梯房/工体/三里屯/东大桥/朝阳医院/世贸天阶/国贸</td>\n",
       "      <td>147335664</td>\n",
       "      <td>Pony</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>0.500251</td>\n",
       "      <td>0.034476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.255505</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.826680</td>\n",
       "      <td>0.422779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25812</th>\n",
       "      <td>33950006</td>\n",
       "      <td>临近地铁温馨网红风小屋一居室</td>\n",
       "      <td>141786513</td>\n",
       "      <td>昊</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.90625</td>\n",
       "      <td>116.5000</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.201327</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.445191</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>-1.206644</td>\n",
       "      <td>1.045994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25813</th>\n",
       "      <td>33950535</td>\n",
       "      <td>3. 老国展,三元桥地铁,静安东里大床房</td>\n",
       "      <td>213500128</td>\n",
       "      <td>晓征</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>39.96875</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Private room</td>\n",
       "      <td>-0.302995</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.255505</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>-1.206644</td>\n",
       "      <td>0.266781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25814</th>\n",
       "      <td>33954414</td>\n",
       "      <td>密码锁自行入住,隐私安全,丰台宋家庄交通枢纽站,去往北京站北京南站,天安门故宫,长城水魔方,...</td>\n",
       "      <td>252799678</td>\n",
       "      <td>超</td>\n",
       "      <td>丰台区</td>\n",
       "      <td>39.84375</td>\n",
       "      <td>116.4375</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>-0.225843</td>\n",
       "      <td>-0.107866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.255505</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>-1.206644</td>\n",
       "      <td>0.238997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25815 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                               name    host_id  \\\n",
       "0         44054               Modern and Comfortable Living in CBD     192875   \n",
       "1        128496                Heart of Beijing: House with View 2     467520   \n",
       "2        161902                   cozy studio in center of Beijing     707535   \n",
       "3        162144                  nice studio near subway, sleep 4      707535   \n",
       "4        279078                          Nice Apartment in Beijing    1455726   \n",
       "...         ...                                                ...        ...   \n",
       "25810  33948728                            望京西门子,798,央美附近 温馨如家大床民宿  256112655   \n",
       "25811  33948787                  04简约舒适电梯房/工体/三里屯/东大桥/朝阳医院/世贸天阶/国贸  147335664   \n",
       "25812  33950006                                     临近地铁温馨网红风小屋一居室  141786513   \n",
       "25813  33950535                               3. 老国展,三元桥地铁,静安东里大床房  213500128   \n",
       "25814  33954414  密码锁自行入住,隐私安全,丰台宋家庄交通枢纽站,去往北京站北京南站,天安门故宫,长城水魔方,...  252799678   \n",
       "\n",
       "             host_name neighbourhood  latitude  longitude        room_type  \\\n",
       "0      East Apartments           朝阳区  39.90625   116.4375  Entire home/apt   \n",
       "1                Cindy           东城区  39.93750   116.4375  Entire home/apt   \n",
       "2               Robert           东城区  39.93750   116.4375  Entire home/apt   \n",
       "3               Robert           朝阳区  39.93750   116.4375  Entire home/apt   \n",
       "4                Fiona           东城区  39.93750   116.4375  Entire home/apt   \n",
       "...                ...           ...       ...        ...              ...   \n",
       "25810                旭           朝阳区  40.00000   116.5000  Entire home/apt   \n",
       "25811             Pony           朝阳区  39.93750   116.4375  Entire home/apt   \n",
       "25812                昊           朝阳区  39.90625   116.5000  Entire home/apt   \n",
       "25813               晓征           朝阳区  39.96875   116.4375     Private room   \n",
       "25814                超           丰台区  39.84375   116.4375  Entire home/apt   \n",
       "\n",
       "          price  minimum_nights  ...  host_id__count       today  \\\n",
       "0      0.132517       -0.107866  ...       -0.141694  2020-05-15   \n",
       "1     -0.158064        0.034476  ...       -0.445191  2020-05-15   \n",
       "2     -0.167438       -0.107866  ...       -0.293442  2020-05-15   \n",
       "3     -0.051350       -0.107866  ...       -0.293442  2020-05-15   \n",
       "4     -0.147970       -0.107866  ...       -0.217568  2020-05-15   \n",
       "...         ...             ...  ...             ...         ...   \n",
       "25810 -0.153017       -0.107866  ...       -0.445191  2020-05-15   \n",
       "25811  0.500251        0.034476  ...       -0.255505  2020-05-15   \n",
       "25812 -0.201327       -0.107866  ...       -0.445191  2020-05-15   \n",
       "25813 -0.302995       -0.107866  ...       -0.255505  2020-05-15   \n",
       "25814 -0.225843       -0.107866  ...       -0.255505  2020-05-15   \n",
       "\n",
       "       last_rewtonow  convenience   hotness  reputation  experience  \\\n",
       "0           0.827420     1.010856  2.113001    0.146469    0.405785   \n",
       "1          -1.206644     0.141861  5.847522    0.758745    0.573680   \n",
       "2           0.827932     0.506329  0.289447    0.207481    0.297135   \n",
       "3           0.843000     1.028425  0.823387    0.083524    0.366994   \n",
       "4           0.831500     0.606953  0.369097   -0.068295    0.134279   \n",
       "...              ...          ...       ...         ...         ...   \n",
       "25810      -1.206644     0.608361       NaN         NaN         NaN   \n",
       "25811       0.826680     0.422779       NaN         NaN         NaN   \n",
       "25812      -1.206644     1.045994       NaN         NaN         NaN   \n",
       "25813      -1.206644     0.266781       NaN         NaN         NaN   \n",
       "25814      -1.206644     0.238997       NaN         NaN         NaN   \n",
       "\n",
       "       cost_performance recommendation host_quanlity  \n",
       "0              3.062132       2.075402      0.745144  \n",
       "1             -3.629407      -0.473095     -0.456353  \n",
       "2             -1.774598      -0.740269     -0.472173  \n",
       "3             -7.146972      -3.298710     -1.495550  \n",
       "4             -0.907477      -0.339635     -0.266395  \n",
       "...                 ...            ...           ...  \n",
       "25810               NaN            NaN           NaN  \n",
       "25811               NaN            NaN           NaN  \n",
       "25812               NaN            NaN           NaN  \n",
       "25813               NaN            NaN           NaN  \n",
       "25814               NaN            NaN           NaN  \n",
       "\n",
       "[25815 rows x 35 columns]"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf.to_csv(\"newdf_indicator.csv\",index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 房源分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房源位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "indicator= ['convenience',\n",
    " 'hotness',\n",
    " 'reputation',\n",
    " 'experience',\n",
    " 'cost_performance',\n",
    " 'recommendation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbourhood_gr=newdf.groupby(\"neighbourhood\")[indicator].mean().sort_values(by='recommendation',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "for i in indicator:\n",
    "    neighbourhood_gr[i]=mms.fit_transform(neighbourhood_gr[i].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>convenience</th>\n",
       "      <th>hotness</th>\n",
       "      <th>reputation</th>\n",
       "      <th>experience</th>\n",
       "      <th>cost_performance</th>\n",
       "      <th>recommendation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>门头沟区</th>\n",
       "      <td>0.198562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>延庆区</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032649</td>\n",
       "      <td>0.282043</td>\n",
       "      <td>0.122346</td>\n",
       "      <td>0.980912</td>\n",
       "      <td>0.986470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大兴区</th>\n",
       "      <td>0.165247</td>\n",
       "      <td>0.272973</td>\n",
       "      <td>0.569432</td>\n",
       "      <td>0.292341</td>\n",
       "      <td>0.900407</td>\n",
       "      <td>0.925912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>怀柔区</th>\n",
       "      <td>0.099760</td>\n",
       "      <td>0.147957</td>\n",
       "      <td>0.975847</td>\n",
       "      <td>0.476218</td>\n",
       "      <td>0.837558</td>\n",
       "      <td>0.858707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西城区</th>\n",
       "      <td>0.109862</td>\n",
       "      <td>0.678617</td>\n",
       "      <td>0.784498</td>\n",
       "      <td>0.315350</td>\n",
       "      <td>0.796267</td>\n",
       "      <td>0.848090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               convenience   hotness  reputation  experience  \\\n",
       "neighbourhood                                                  \n",
       "门头沟区              0.198562  0.000000    0.000000    0.000000   \n",
       "延庆区               0.000000  0.032649    0.282043    0.122346   \n",
       "大兴区               0.165247  0.272973    0.569432    0.292341   \n",
       "怀柔区               0.099760  0.147957    0.975847    0.476218   \n",
       "西城区               0.109862  0.678617    0.784498    0.315350   \n",
       "\n",
       "               cost_performance  recommendation  \n",
       "neighbourhood                                    \n",
       "门头沟区                   1.000000        1.000000  \n",
       "延庆区                    0.980912        0.986470  \n",
       "大兴区                    0.900407        0.925912  \n",
       "怀柔区                    0.837558        0.858707  \n",
       "西城区                    0.796267        0.848090  "
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbourhood_gr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "new Promise(function(resolve, reject) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    script.onload = resolve;\n",
       "    script.onerror = reject;\n",
       "    script.src = \"https://assets.pyecharts.org/assets/echarts.min.js\";\n",
       "    document.head.appendChild(script);\n",
       "}).then(() => {\n",
       "new Promise(function(resolve, reject) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    script.onload = resolve;\n",
       "    script.onerror = reject;\n",
       "    script.src = \"https://assets.pyecharts.org/assets/maps/beijing.js\";\n",
       "    document.head.appendChild(script);\n",
       "}).then(() => {\n",
       "\n",
       "});\n",
       "});"
      ],
      "text/plain": [
       "<pyecharts.render.display.Javascript at 0x19b03121088>"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.globals import CurrentConfig, NotebookType\n",
    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts.faker import Faker\n",
    "\n",
    "c = (\n",
    "    Map()\n",
    "    .add(\"neighbourhood\", [list(z) for z in zip(neighbourhood_gr.index, neighbourhood_gr.experience)], \"北京\")\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"Map-北京地图\"), visualmap_opts=opts.VisualMapOpts(max_=1, is_piecewise=True)\n",
    "    )\n",
    "\n",
    ")\n",
    "c.load_javascript()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"241a866e2afb4027afd687d790cd04a0\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_241a866e2afb4027afd687d790cd04a0 = echarts.init(\n",
       "            document.getElementById('241a866e2afb4027afd687d790cd04a0'), 'white', {renderer: 'canvas'});\n",
       "        var option_241a866e2afb4027afd687d790cd04a0 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"map\",\n",
       "            \"name\": \"neighbourhood\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"mapType\": \"\\u5317\\u4eac\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u95e8\\u5934\\u6c9f\\u533a\",\n",
       "                    \"value\": 0.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5ef6\\u5e86\\u533a\",\n",
       "                    \"value\": 0.12234615372438773\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5927\\u5174\\u533a\",\n",
       "                    \"value\": 0.2923406173274333\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6000\\u67d4\\u533a\",\n",
       "                    \"value\": 0.47621808838675855\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u897f\\u57ce\\u533a\",\n",
       "                    \"value\": 0.3153498299682671\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d77\\u6dc0\\u533a\",\n",
       "                    \"value\": 0.35572906794349357\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u623f\\u5c71\\u533a\",\n",
       "                    \"value\": 0.466374057746256\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u901a\\u5dde\\u533a\",\n",
       "                    \"value\": 0.1813346201835105\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u987a\\u4e49\\u533a\",\n",
       "                    \"value\": 0.18228729824373469\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u660c\\u5e73\\u533a\",\n",
       "                    \"value\": 0.2605386477653291\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e30\\u53f0\\u533a\",\n",
       "                    \"value\": 0.25058611937303826\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5bc6\\u4e91\\u533a\",\n",
       "                    \"value\": 0.14002682222558055\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e1c\\u57ce\\u533a\",\n",
       "                    \"value\": 0.5768653140546549\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e73\\u8c37\\u533a\",\n",
       "                    \"value\": 0.20562866258314852\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u671d\\u9633\\u533a\",\n",
       "                    \"value\": 1.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u77f3\\u666f\\u5c71\\u533a\",\n",
       "                    \"value\": 0.06778263916620163\n",
       "                }\n",
       "            ],\n",
       "            \"roam\": true,\n",
       "            \"zoom\": 1,\n",
       "            \"showLegendSymbol\": true,\n",
       "            \"emphasis\": {}\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"neighbourhood\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"neighbourhood\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Map-\\u5317\\u4eac\\u5730\\u56fe\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"piecewise\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 1,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 14,\n",
       "        \"borderWidth\": 0\n",
       "    }\n",
       "};\n",
       "        chart_241a866e2afb4027afd687d790cd04a0.setOption(option_241a866e2afb4027afd687d790cd04a0);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x19b68403288>"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "new Promise(function(resolve, reject) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    script.onload = resolve;\n",
       "    script.onerror = reject;\n",
       "    script.src = \"https://assets.pyecharts.org/assets/echarts.min.js\";\n",
       "    document.head.appendChild(script);\n",
       "}).then(() => {\n",
       "\n",
       "});"
      ],
      "text/plain": [
       "<pyecharts.render.display.Javascript at 0x19b02eb2ec8>"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.globals import CurrentConfig, NotebookType\n",
    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Radar\n",
    "v1 = [neighbourhood_gr.iloc[0,:].values.tolist()]\n",
    "v2 = [neighbourhood_gr.iloc[1,:].values.tolist()]\n",
    "v3 = [neighbourhood_gr.iloc[2,:].values.tolist()]\n",
    "\n",
    "c = (\n",
    "    Radar()\n",
    "    .add_schema(\n",
    "        schema=[\n",
    "            opts.RadarIndicatorItem(name=\"convenience\", max_=neighbourhood_gr.convenience.max()),\n",
    "            opts.RadarIndicatorItem(name=\"hotness\", max_=neighbourhood_gr.hotness.max()),\n",
    "            opts.RadarIndicatorItem(name=\"reputation\", max_=neighbourhood_gr.reputation.max()),\n",
    "            opts.RadarIndicatorItem(name=\"experience\", max_=neighbourhood_gr.experience.max()),\n",
    "            opts.RadarIndicatorItem(name=\"cost_performance\", max_=neighbourhood_gr.cost_performance.max()),\n",
    "            opts.RadarIndicatorItem(name=\"recommendation\", max_=neighbourhood_gr.recommendation.max()),\n",
    "        ]\n",
    "    )\n",
    "    .add(\"门头沟区\", v1)\n",
    "    .add(\"延庆区\", v2)\n",
    "    .add(\"大兴区\", v3)\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        legend_opts=opts.LegendOpts(selected_mode=\"single\"),\n",
    "        title_opts=opts.TitleOpts(title=\"Radar-单例模式\"),\n",
    "    )\n",
    "\n",
    ")\n",
    "c.load_javascript()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"cf0255f30f44411784440c9ead6e5fba\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_cf0255f30f44411784440c9ead6e5fba = echarts.init(\n",
       "            document.getElementById('cf0255f30f44411784440c9ead6e5fba'), 'white', {renderer: 'canvas'});\n",
       "        var option_cf0255f30f44411784440c9ead6e5fba = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"\\u95e8\\u5934\\u6c9f\\u533a\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0.19856231400967947,\n",
       "                    0.0,\n",
       "                    0.0,\n",
       "                    0.0,\n",
       "                    1.0,\n",
       "                    1.0\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"\\u5ef6\\u5e86\\u533a\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0.0,\n",
       "                    0.03264904795516621,\n",
       "                    0.2820426375689092,\n",
       "                    0.12234615372438773,\n",
       "                    0.9809122381734152,\n",
       "                    0.9864696818231568\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"\\u5927\\u5174\\u533a\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0.1652470877734684,\n",
       "                    0.27297276623347466,\n",
       "                    0.5694321428729161,\n",
       "                    0.2923406173274333,\n",
       "                    0.900406646673936,\n",
       "                    0.925912047460711\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u95e8\\u5934\\u6c9f\\u533a\",\n",
       "                \"\\u5ef6\\u5e86\\u533a\",\n",
       "                \"\\u5927\\u5174\\u533a\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u95e8\\u5934\\u6c9f\\u533a\": true,\n",
       "                \"\\u5ef6\\u5e86\\u533a\": true,\n",
       "                \"\\u5927\\u5174\\u533a\": true\n",
       "            },\n",
       "            \"selectedMode\": \"single\",\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"radar\": {\n",
       "        \"indicator\": [\n",
       "            {\n",
       "                \"name\": \"convenience\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"hotness\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"reputation\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"experience\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"cost_performance\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"recommendation\",\n",
       "                \"max\": 1.0\n",
       "            }\n",
       "        ],\n",
       "        \"name\": {\n",
       "            \"textStyle\": {}\n",
       "        },\n",
       "        \"splitLine\": {\n",
       "            \"show\": true,\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            }\n",
       "        },\n",
       "        \"splitArea\": {\n",
       "            \"show\": true,\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            }\n",
       "        },\n",
       "        \"axisLine\": {\n",
       "            \"show\": true,\n",
       "            \"onZero\": true,\n",
       "            \"onZeroAxisIndex\": 0\n",
       "        }\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Radar-\\u5355\\u4f8b\\u6a21\\u5f0f\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_cf0255f30f44411784440c9ead6e5fba.setOption(option_cf0255f30f44411784440c9ead6e5fba);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x19b030d7908>"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房屋类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "roomtype_gr=newdf.groupby(\"room_type\")[indicator].mean().sort_values(by='recommendation',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "for i in indicator:\n",
    "    roomtype_gr[i]=mms.fit_transform(roomtype_gr[i].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>convenience</th>\n",
       "      <th>hotness</th>\n",
       "      <th>reputation</th>\n",
       "      <th>experience</th>\n",
       "      <th>cost_performance</th>\n",
       "      <th>recommendation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Shared room</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.281613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private room</th>\n",
       "      <td>0.374969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.751628</td>\n",
       "      <td>0.827321</td>\n",
       "      <td>0.900815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entire home/apt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014985</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 convenience   hotness  reputation  experience  \\\n",
       "room_type                                                        \n",
       "Shared room         0.000000  0.000000    0.281613    0.000000   \n",
       "Private room        0.374969  1.000000    1.000000    0.751628   \n",
       "Entire home/apt     1.000000  0.014985    0.000000    1.000000   \n",
       "\n",
       "                 cost_performance  recommendation  \n",
       "room_type                                          \n",
       "Shared room              1.000000        1.000000  \n",
       "Private room             0.827321        0.900815  \n",
       "Entire home/apt          0.000000        0.000000  "
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roomtype_gr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "new Promise(function(resolve, reject) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    script.onload = resolve;\n",
       "    script.onerror = reject;\n",
       "    script.src = \"https://assets.pyecharts.org/assets/echarts.min.js\";\n",
       "    document.head.appendChild(script);\n",
       "}).then(() => {\n",
       "\n",
       "});"
      ],
      "text/plain": [
       "<pyecharts.render.display.Javascript at 0x19b6871d808>"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.globals import CurrentConfig, NotebookType\n",
    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Radar\n",
    "v1 = [roomtype_gr.iloc[0,:].values.tolist()]\n",
    "v2 = [roomtype_gr.iloc[1,:].values.tolist()]\n",
    "v3 = [roomtype_gr.iloc[2,:].values.tolist()]\n",
    "\n",
    "c = (\n",
    "    Radar()\n",
    "    .add_schema(\n",
    "        schema=[\n",
    "            opts.RadarIndicatorItem(name=\"convenience\", max_=roomtype_gr.convenience.max()),\n",
    "            opts.RadarIndicatorItem(name=\"hotness\", max_=roomtype_gr.hotness.max()),\n",
    "            opts.RadarIndicatorItem(name=\"reputation\", max_=roomtype_gr.reputation.max()),\n",
    "            opts.RadarIndicatorItem(name=\"experience\", max_=roomtype_gr.experience.max()),\n",
    "            opts.RadarIndicatorItem(name=\"cost_performance\", max_=roomtype_gr.cost_performance.max()),\n",
    "            opts.RadarIndicatorItem(name=\"recommendation\", max_=roomtype_gr.recommendation.max()),\n",
    "        ]\n",
    "    )\n",
    "    .add(\"host_quanlity1\", v1)\n",
    "    .add(\"host_quanlity2\", v2)\n",
    "    .add(\"host_quanlity3\", v3)\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        legend_opts=opts.LegendOpts(selected_mode=\"single\"),\n",
    "        title_opts=opts.TitleOpts(title=\"Radar-单例模式\"),\n",
    "    )\n",
    "\n",
    ")\n",
    "c.load_javascript()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"65f49d7af13f46a898baa0166000e540\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_65f49d7af13f46a898baa0166000e540 = echarts.init(\n",
       "            document.getElementById('65f49d7af13f46a898baa0166000e540'), 'white', {renderer: 'canvas'});\n",
       "        var option_65f49d7af13f46a898baa0166000e540 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"host_quanlity1\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0.0,\n",
       "                    0.0,\n",
       "                    0.2816127445355662,\n",
       "                    0.0,\n",
       "                    1.0000000000000002,\n",
       "                    1.0000000000000002\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"host_quanlity2\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0.37496917346293823,\n",
       "                    0.9999999999999998,\n",
       "                    1.0,\n",
       "                    0.7516276466481286,\n",
       "                    0.8273206287853238,\n",
       "                    0.9008145022254667\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"host_quanlity3\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    1.0,\n",
       "                    0.014984783352890219,\n",
       "                    0.0,\n",
       "                    1.0,\n",
       "                    0.0,\n",
       "                    0.0\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"host_quanlity1\",\n",
       "                \"host_quanlity2\",\n",
       "                \"host_quanlity3\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"host_quanlity1\": true,\n",
       "                \"host_quanlity2\": true,\n",
       "                \"host_quanlity3\": true\n",
       "            },\n",
       "            \"selectedMode\": \"single\",\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"radar\": {\n",
       "        \"indicator\": [\n",
       "            {\n",
       "                \"name\": \"convenience\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"hotness\",\n",
       "                \"max\": 0.9999999999999998\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"reputation\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"experience\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"cost_performance\",\n",
       "                \"max\": 1.0000000000000002\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"recommendation\",\n",
       "                \"max\": 1.0000000000000002\n",
       "            }\n",
       "        ],\n",
       "        \"name\": {\n",
       "            \"textStyle\": {}\n",
       "        },\n",
       "        \"splitLine\": {\n",
       "            \"show\": true,\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            }\n",
       "        },\n",
       "        \"splitArea\": {\n",
       "            \"show\": true,\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            }\n",
       "        },\n",
       "        \"axisLine\": {\n",
       "            \"show\": true,\n",
       "            \"onZero\": true,\n",
       "            \"onZeroAxisIndex\": 0\n",
       "        }\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Radar-\\u5355\\u4f8b\\u6a21\\u5f0f\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_65f49d7af13f46a898baa0166000e540.setOption(option_65f49d7af13f46a898baa0166000e540);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x19b09d51648>"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 交叉分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"21\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">convenience</th>\n",
       "      <th colspan=\"3\" halign=\"left\">cost_performance</th>\n",
       "      <th colspan=\"3\" halign=\"left\">experience</th>\n",
       "      <th colspan=\"3\" halign=\"left\">hotness</th>\n",
       "      <th colspan=\"3\" halign=\"left\">price</th>\n",
       "      <th colspan=\"3\" halign=\"left\">recommendation</th>\n",
       "      <th colspan=\"3\" halign=\"left\">reputation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_type</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>...</th>\n",
       "      <th>Shared room</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>东城区</th>\n",
       "      <td>0.354251</td>\n",
       "      <td>0.003144</td>\n",
       "      <td>-0.201467</td>\n",
       "      <td>-0.921186</td>\n",
       "      <td>-0.918392</td>\n",
       "      <td>0.126035</td>\n",
       "      <td>0.121170</td>\n",
       "      <td>0.081610</td>\n",
       "      <td>-0.046487</td>\n",
       "      <td>0.266443</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219825</td>\n",
       "      <td>0.111495</td>\n",
       "      <td>-0.024687</td>\n",
       "      <td>-0.318488</td>\n",
       "      <td>-0.370953</td>\n",
       "      <td>-0.329884</td>\n",
       "      <td>0.093036</td>\n",
       "      <td>0.016065</td>\n",
       "      <td>0.120441</td>\n",
       "      <td>0.012982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>丰台区</th>\n",
       "      <td>0.346734</td>\n",
       "      <td>-0.050243</td>\n",
       "      <td>-0.308848</td>\n",
       "      <td>-0.690218</td>\n",
       "      <td>-0.108831</td>\n",
       "      <td>0.565380</td>\n",
       "      <td>0.042360</td>\n",
       "      <td>0.026486</td>\n",
       "      <td>-0.219920</td>\n",
       "      <td>-0.117389</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.198761</td>\n",
       "      <td>-0.106406</td>\n",
       "      <td>-0.251472</td>\n",
       "      <td>-0.344573</td>\n",
       "      <td>-0.355879</td>\n",
       "      <td>-0.040002</td>\n",
       "      <td>0.176962</td>\n",
       "      <td>-0.078171</td>\n",
       "      <td>0.055537</td>\n",
       "      <td>-0.153223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大兴区</th>\n",
       "      <td>0.283706</td>\n",
       "      <td>-0.026294</td>\n",
       "      <td>-0.343567</td>\n",
       "      <td>0.049373</td>\n",
       "      <td>1.780716</td>\n",
       "      <td>0.692357</td>\n",
       "      <td>0.059387</td>\n",
       "      <td>0.008959</td>\n",
       "      <td>-0.231963</td>\n",
       "      <td>-0.229180</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.366074</td>\n",
       "      <td>-0.045375</td>\n",
       "      <td>-0.220056</td>\n",
       "      <td>-0.188769</td>\n",
       "      <td>-0.003334</td>\n",
       "      <td>0.844466</td>\n",
       "      <td>0.203375</td>\n",
       "      <td>-0.043918</td>\n",
       "      <td>0.015238</td>\n",
       "      <td>-0.201370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>密云区</th>\n",
       "      <td>0.260666</td>\n",
       "      <td>-0.018827</td>\n",
       "      <td>-0.181909</td>\n",
       "      <td>-0.823313</td>\n",
       "      <td>0.401355</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049940</td>\n",
       "      <td>-0.103640</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.224834</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428038</td>\n",
       "      <td>-0.010442</td>\n",
       "      <td>-0.327150</td>\n",
       "      <td>-0.441641</td>\n",
       "      <td>0.101058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.030600</td>\n",
       "      <td>-0.138741</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平谷区</th>\n",
       "      <td>0.241275</td>\n",
       "      <td>-0.133032</td>\n",
       "      <td>-0.517964</td>\n",
       "      <td>-0.940230</td>\n",
       "      <td>0.725149</td>\n",
       "      <td>0.421993</td>\n",
       "      <td>0.050803</td>\n",
       "      <td>-0.086212</td>\n",
       "      <td>-0.160419</td>\n",
       "      <td>-0.332911</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.365174</td>\n",
       "      <td>0.618708</td>\n",
       "      <td>0.198523</td>\n",
       "      <td>-0.359236</td>\n",
       "      <td>-0.521456</td>\n",
       "      <td>0.227379</td>\n",
       "      <td>0.089836</td>\n",
       "      <td>-0.026718</td>\n",
       "      <td>-0.072016</td>\n",
       "      <td>0.113517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>延庆区</th>\n",
       "      <td>0.265884</td>\n",
       "      <td>-0.114509</td>\n",
       "      <td>-0.553766</td>\n",
       "      <td>-0.775649</td>\n",
       "      <td>4.448827</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057918</td>\n",
       "      <td>-0.127143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.414252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.807420</td>\n",
       "      <td>0.075653</td>\n",
       "      <td>0.442086</td>\n",
       "      <td>-0.453300</td>\n",
       "      <td>2.094217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.036431</td>\n",
       "      <td>-0.172534</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>怀柔区</th>\n",
       "      <td>0.305253</td>\n",
       "      <td>-0.024302</td>\n",
       "      <td>-0.267962</td>\n",
       "      <td>0.234437</td>\n",
       "      <td>0.277515</td>\n",
       "      <td>-0.951910</td>\n",
       "      <td>0.124899</td>\n",
       "      <td>0.032066</td>\n",
       "      <td>-0.923021</td>\n",
       "      <td>-0.313859</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.550079</td>\n",
       "      <td>1.124750</td>\n",
       "      <td>0.238989</td>\n",
       "      <td>0.531184</td>\n",
       "      <td>0.091916</td>\n",
       "      <td>0.075315</td>\n",
       "      <td>-0.862877</td>\n",
       "      <td>0.034633</td>\n",
       "      <td>0.053293</td>\n",
       "      <td>-1.238671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房山区</th>\n",
       "      <td>0.262572</td>\n",
       "      <td>-0.060431</td>\n",
       "      <td>-0.463172</td>\n",
       "      <td>-0.137546</td>\n",
       "      <td>-0.027444</td>\n",
       "      <td>0.805121</td>\n",
       "      <td>0.111957</td>\n",
       "      <td>-0.015451</td>\n",
       "      <td>-0.221826</td>\n",
       "      <td>-0.204081</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.459020</td>\n",
       "      <td>0.138097</td>\n",
       "      <td>-0.116122</td>\n",
       "      <td>-0.051530</td>\n",
       "      <td>-0.075905</td>\n",
       "      <td>-0.108827</td>\n",
       "      <td>0.244209</td>\n",
       "      <td>0.041043</td>\n",
       "      <td>0.005954</td>\n",
       "      <td>-0.104324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>昌平区</th>\n",
       "      <td>0.303419</td>\n",
       "      <td>-0.114991</td>\n",
       "      <td>-0.444453</td>\n",
       "      <td>0.044251</td>\n",
       "      <td>-0.742922</td>\n",
       "      <td>0.727920</td>\n",
       "      <td>0.038776</td>\n",
       "      <td>0.048362</td>\n",
       "      <td>-0.258091</td>\n",
       "      <td>-0.306714</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.362679</td>\n",
       "      <td>0.790067</td>\n",
       "      <td>-0.107323</td>\n",
       "      <td>-0.275319</td>\n",
       "      <td>-0.027585</td>\n",
       "      <td>-0.415593</td>\n",
       "      <td>0.213997</td>\n",
       "      <td>-0.077468</td>\n",
       "      <td>0.101980</td>\n",
       "      <td>-0.190680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>朝阳区</th>\n",
       "      <td>0.804830</td>\n",
       "      <td>0.441733</td>\n",
       "      <td>0.155820</td>\n",
       "      <td>-2.196557</td>\n",
       "      <td>-0.616735</td>\n",
       "      <td>-0.150824</td>\n",
       "      <td>0.230880</td>\n",
       "      <td>0.192742</td>\n",
       "      <td>0.037654</td>\n",
       "      <td>0.354803</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245314</td>\n",
       "      <td>0.009955</td>\n",
       "      <td>-0.216663</td>\n",
       "      <td>-0.319876</td>\n",
       "      <td>-0.958032</td>\n",
       "      <td>-0.180193</td>\n",
       "      <td>-0.015053</td>\n",
       "      <td>-0.023876</td>\n",
       "      <td>0.085403</td>\n",
       "      <td>-0.004725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海淀区</th>\n",
       "      <td>0.406193</td>\n",
       "      <td>0.027167</td>\n",
       "      <td>-0.287363</td>\n",
       "      <td>-0.445394</td>\n",
       "      <td>0.150475</td>\n",
       "      <td>-0.091612</td>\n",
       "      <td>0.083526</td>\n",
       "      <td>0.033811</td>\n",
       "      <td>-0.041226</td>\n",
       "      <td>-0.059545</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.083095</td>\n",
       "      <td>0.018461</td>\n",
       "      <td>-0.233129</td>\n",
       "      <td>-0.175115</td>\n",
       "      <td>-0.209548</td>\n",
       "      <td>0.093673</td>\n",
       "      <td>-0.074793</td>\n",
       "      <td>-0.057666</td>\n",
       "      <td>0.037490</td>\n",
       "      <td>0.076081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>石景山区</th>\n",
       "      <td>0.302248</td>\n",
       "      <td>-0.134725</td>\n",
       "      <td>-0.579780</td>\n",
       "      <td>-5.396555</td>\n",
       "      <td>-0.531924</td>\n",
       "      <td>3.780152</td>\n",
       "      <td>0.098090</td>\n",
       "      <td>-0.167021</td>\n",
       "      <td>-1.227003</td>\n",
       "      <td>-0.233409</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.376083</td>\n",
       "      <td>-0.105725</td>\n",
       "      <td>-0.194625</td>\n",
       "      <td>-0.320609</td>\n",
       "      <td>-2.715532</td>\n",
       "      <td>-0.354504</td>\n",
       "      <td>1.446759</td>\n",
       "      <td>0.025440</td>\n",
       "      <td>-0.200704</td>\n",
       "      <td>-1.496178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西城区</th>\n",
       "      <td>0.277923</td>\n",
       "      <td>-0.041562</td>\n",
       "      <td>-0.386467</td>\n",
       "      <td>-0.603560</td>\n",
       "      <td>1.381999</td>\n",
       "      <td>0.227727</td>\n",
       "      <td>0.079525</td>\n",
       "      <td>-0.011675</td>\n",
       "      <td>-0.091650</td>\n",
       "      <td>0.045861</td>\n",
       "      <td>...</td>\n",
       "      <td>0.283036</td>\n",
       "      <td>0.055379</td>\n",
       "      <td>-0.060479</td>\n",
       "      <td>-0.338686</td>\n",
       "      <td>-0.268750</td>\n",
       "      <td>0.717072</td>\n",
       "      <td>0.142976</td>\n",
       "      <td>-0.003213</td>\n",
       "      <td>0.017402</td>\n",
       "      <td>0.043395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>通州区</th>\n",
       "      <td>0.372073</td>\n",
       "      <td>-0.045465</td>\n",
       "      <td>-0.356872</td>\n",
       "      <td>-0.283448</td>\n",
       "      <td>0.346541</td>\n",
       "      <td>0.174208</td>\n",
       "      <td>0.012814</td>\n",
       "      <td>0.022975</td>\n",
       "      <td>-0.074896</td>\n",
       "      <td>-0.362370</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.447522</td>\n",
       "      <td>-0.127164</td>\n",
       "      <td>-0.249661</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.210354</td>\n",
       "      <td>0.130649</td>\n",
       "      <td>-0.024869</td>\n",
       "      <td>-0.138217</td>\n",
       "      <td>0.048590</td>\n",
       "      <td>0.044618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>门头沟区</th>\n",
       "      <td>0.273138</td>\n",
       "      <td>-0.032508</td>\n",
       "      <td>-0.596906</td>\n",
       "      <td>1.096694</td>\n",
       "      <td>0.638291</td>\n",
       "      <td>1.069473</td>\n",
       "      <td>-0.027026</td>\n",
       "      <td>0.030863</td>\n",
       "      <td>-0.417353</td>\n",
       "      <td>-0.459544</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.545021</td>\n",
       "      <td>0.091544</td>\n",
       "      <td>0.207068</td>\n",
       "      <td>-0.365966</td>\n",
       "      <td>0.448331</td>\n",
       "      <td>0.245448</td>\n",
       "      <td>0.300527</td>\n",
       "      <td>-0.155458</td>\n",
       "      <td>0.040482</td>\n",
       "      <td>-0.293740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顺义区</th>\n",
       "      <td>0.247353</td>\n",
       "      <td>-0.038379</td>\n",
       "      <td>-0.317277</td>\n",
       "      <td>0.133346</td>\n",
       "      <td>-1.779667</td>\n",
       "      <td>0.566182</td>\n",
       "      <td>0.014032</td>\n",
       "      <td>0.018876</td>\n",
       "      <td>-0.107129</td>\n",
       "      <td>-0.203319</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018119</td>\n",
       "      <td>0.119642</td>\n",
       "      <td>-0.095600</td>\n",
       "      <td>-0.176123</td>\n",
       "      <td>0.030219</td>\n",
       "      <td>-0.916282</td>\n",
       "      <td>0.254576</td>\n",
       "      <td>-0.086445</td>\n",
       "      <td>0.051643</td>\n",
       "      <td>-0.006235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean                                            \\\n",
       "                  convenience                          cost_performance   \n",
       "room_type     Entire home/apt Private room Shared room  Entire home/apt   \n",
       "neighbourhood                                                             \n",
       "东城区                  0.354251     0.003144   -0.201467        -0.921186   \n",
       "丰台区                  0.346734    -0.050243   -0.308848        -0.690218   \n",
       "大兴区                  0.283706    -0.026294   -0.343567         0.049373   \n",
       "密云区                  0.260666    -0.018827   -0.181909        -0.823313   \n",
       "平谷区                  0.241275    -0.133032   -0.517964        -0.940230   \n",
       "延庆区                  0.265884    -0.114509   -0.553766        -0.775649   \n",
       "怀柔区                  0.305253    -0.024302   -0.267962         0.234437   \n",
       "房山区                  0.262572    -0.060431   -0.463172        -0.137546   \n",
       "昌平区                  0.303419    -0.114991   -0.444453         0.044251   \n",
       "朝阳区                  0.804830     0.441733    0.155820        -2.196557   \n",
       "海淀区                  0.406193     0.027167   -0.287363        -0.445394   \n",
       "石景山区                 0.302248    -0.134725   -0.579780        -5.396555   \n",
       "西城区                  0.277923    -0.041562   -0.386467        -0.603560   \n",
       "通州区                  0.372073    -0.045465   -0.356872        -0.283448   \n",
       "门头沟区                 0.273138    -0.032508   -0.596906         1.096694   \n",
       "顺义区                  0.247353    -0.038379   -0.317277         0.133346   \n",
       "\n",
       "                                                                     \\\n",
       "                                            experience                \n",
       "room_type     Private room Shared room Entire home/apt Private room   \n",
       "neighbourhood                                                         \n",
       "东城区              -0.918392    0.126035        0.121170     0.081610   \n",
       "丰台区              -0.108831    0.565380        0.042360     0.026486   \n",
       "大兴区               1.780716    0.692357        0.059387     0.008959   \n",
       "密云区               0.401355    0.000000        0.049940    -0.103640   \n",
       "平谷区               0.725149    0.421993        0.050803    -0.086212   \n",
       "延庆区               4.448827    0.000000        0.057918    -0.127143   \n",
       "怀柔区               0.277515   -0.951910        0.124899     0.032066   \n",
       "房山区              -0.027444    0.805121        0.111957    -0.015451   \n",
       "昌平区              -0.742922    0.727920        0.038776     0.048362   \n",
       "朝阳区              -0.616735   -0.150824        0.230880     0.192742   \n",
       "海淀区               0.150475   -0.091612        0.083526     0.033811   \n",
       "石景山区             -0.531924    3.780152        0.098090    -0.167021   \n",
       "西城区               1.381999    0.227727        0.079525    -0.011675   \n",
       "通州区               0.346541    0.174208        0.012814     0.022975   \n",
       "门头沟区              0.638291    1.069473       -0.027026     0.030863   \n",
       "顺义区              -1.779667    0.566182        0.014032     0.018876   \n",
       "\n",
       "                                           ...                              \\\n",
       "                                  hotness  ...                       price   \n",
       "room_type     Shared room Entire home/apt  ... Shared room Entire home/apt   \n",
       "neighbourhood                              ...                               \n",
       "东城区             -0.046487        0.266443  ...    0.219825        0.111495   \n",
       "丰台区             -0.219920       -0.117389  ...   -0.198761       -0.106406   \n",
       "大兴区             -0.231963       -0.229180  ...   -0.366074       -0.045375   \n",
       "密云区              0.000000       -0.224834  ...    0.000000        0.428038   \n",
       "平谷区             -0.160419       -0.332911  ...   -0.365174        0.618708   \n",
       "延庆区              0.000000       -0.414252  ...    0.000000        0.807420   \n",
       "怀柔区             -0.923021       -0.313859  ...   -0.550079        1.124750   \n",
       "房山区             -0.221826       -0.204081  ...   -0.459020        0.138097   \n",
       "昌平区             -0.258091       -0.306714  ...   -0.362679        0.790067   \n",
       "朝阳区              0.037654        0.354803  ...    0.245314        0.009955   \n",
       "海淀区             -0.041226       -0.059545  ...   -0.083095        0.018461   \n",
       "石景山区            -1.227003       -0.233409  ...   -0.376083       -0.105725   \n",
       "西城区             -0.091650        0.045861  ...    0.283036        0.055379   \n",
       "通州区             -0.074896       -0.362370  ...   -0.447522       -0.127164   \n",
       "门头沟区            -0.417353       -0.459544  ...   -0.545021        0.091544   \n",
       "顺义区             -0.107129       -0.203319  ...    0.018119        0.119642   \n",
       "\n",
       "                                                                     \\\n",
       "                                        recommendation                \n",
       "room_type     Private room Shared room Entire home/apt Private room   \n",
       "neighbourhood                                                         \n",
       "东城区              -0.024687   -0.318488       -0.370953    -0.329884   \n",
       "丰台区              -0.251472   -0.344573       -0.355879    -0.040002   \n",
       "大兴区              -0.220056   -0.188769       -0.003334     0.844466   \n",
       "密云区              -0.010442   -0.327150       -0.441641     0.101058   \n",
       "平谷区               0.198523   -0.359236       -0.521456     0.227379   \n",
       "延庆区               0.075653    0.442086       -0.453300     2.094217   \n",
       "怀柔区               0.238989    0.531184        0.091916     0.075315   \n",
       "房山区              -0.116122   -0.051530       -0.075905    -0.108827   \n",
       "昌平区              -0.107323   -0.275319       -0.027585    -0.415593   \n",
       "朝阳区              -0.216663   -0.319876       -0.958032    -0.180193   \n",
       "海淀区              -0.233129   -0.175115       -0.209548     0.093673   \n",
       "石景山区             -0.194625   -0.320609       -2.715532    -0.354504   \n",
       "西城区              -0.060479   -0.338686       -0.268750     0.717072   \n",
       "通州区              -0.249661   -0.325930       -0.210354     0.130649   \n",
       "门头沟区              0.207068   -0.365966        0.448331     0.245448   \n",
       "顺义区              -0.095600   -0.176123        0.030219    -0.916282   \n",
       "\n",
       "                                                                    \n",
       "                               reputation                           \n",
       "room_type     Shared room Entire home/apt Private room Shared room  \n",
       "neighbourhood                                                       \n",
       "东城区              0.093036        0.016065     0.120441    0.012982  \n",
       "丰台区              0.176962       -0.078171     0.055537   -0.153223  \n",
       "大兴区              0.203375       -0.043918     0.015238   -0.201370  \n",
       "密云区              0.000000       -0.030600    -0.138741    0.000000  \n",
       "平谷区              0.089836       -0.026718    -0.072016    0.113517  \n",
       "延庆区              0.000000       -0.036431    -0.172534    0.000000  \n",
       "怀柔区             -0.862877        0.034633     0.053293   -1.238671  \n",
       "房山区              0.244209        0.041043     0.005954   -0.104324  \n",
       "昌平区              0.213997       -0.077468     0.101980   -0.190680  \n",
       "朝阳区             -0.015053       -0.023876     0.085403   -0.004725  \n",
       "海淀区             -0.074793       -0.057666     0.037490    0.076081  \n",
       "石景山区             1.446759        0.025440    -0.200704   -1.496178  \n",
       "西城区              0.142976       -0.003213     0.017402    0.043395  \n",
       "通州区             -0.024869       -0.138217     0.048590    0.044618  \n",
       "门头沟区             0.300527       -0.155458     0.040482   -0.293740  \n",
       "顺义区              0.254576       -0.086445     0.051643   -0.006235  \n",
       "\n",
       "[16 rows x 21 columns]"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(newdf,index=[\"neighbourhood\"],values=[\"price\"]+indicator,columns=[\"room_type\"],aggfunc=[np.mean],fill_value=0,margins=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 房东分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "房东分析：量化房东旗下房源数量，房源位置，房源价格，可租天数、房源评价等信息，为房东计算质量分数。有了这个质量分数，airbnb能够有针对性的维护高质量房东，为他们提供佣金折扣以及各种福利待遇；也能够惩罚低质量房东，督促房东改进房源，提升平台的整理质量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "newdf[\"host_id\"]=le.fit_transform(newdf[\"host_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "host=newdf.groupby(\"host_id\")[indicator+[\"host_quanlity\"]].mean().sort_values(by='host_quanlity',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>convenience</th>\n",
       "      <th>hotness</th>\n",
       "      <th>reputation</th>\n",
       "      <th>experience</th>\n",
       "      <th>cost_performance</th>\n",
       "      <th>recommendation</th>\n",
       "      <th>host_quanlity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5163</th>\n",
       "      <td>-0.101211</td>\n",
       "      <td>-0.694468</td>\n",
       "      <td>-2.420811</td>\n",
       "      <td>-1.724931</td>\n",
       "      <td>213.301148</td>\n",
       "      <td>105.994201</td>\n",
       "      <td>42.153328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>0.970926</td>\n",
       "      <td>3.236059</td>\n",
       "      <td>0.138198</td>\n",
       "      <td>0.388016</td>\n",
       "      <td>193.253340</td>\n",
       "      <td>97.390287</td>\n",
       "      <td>38.689000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4905</th>\n",
       "      <td>0.509570</td>\n",
       "      <td>-0.383209</td>\n",
       "      <td>-2.420811</td>\n",
       "      <td>-1.541697</td>\n",
       "      <td>190.642805</td>\n",
       "      <td>94.782252</td>\n",
       "      <td>37.645786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4781</th>\n",
       "      <td>0.719389</td>\n",
       "      <td>-0.209311</td>\n",
       "      <td>-2.420800</td>\n",
       "      <td>-1.382199</td>\n",
       "      <td>170.919739</td>\n",
       "      <td>85.003347</td>\n",
       "      <td>33.779749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6271</th>\n",
       "      <td>0.792039</td>\n",
       "      <td>-0.061905</td>\n",
       "      <td>-1.901546</td>\n",
       "      <td>-1.169657</td>\n",
       "      <td>144.637169</td>\n",
       "      <td>71.955307</td>\n",
       "      <td>28.537770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         convenience   hotness  reputation  experience  cost_performance  \\\n",
       "host_id                                                                    \n",
       "5163       -0.101211 -0.694468   -2.420811   -1.724931        213.301148   \n",
       "3534        0.970926  3.236059    0.138198    0.388016        193.253340   \n",
       "4905        0.509570 -0.383209   -2.420811   -1.541697        190.642805   \n",
       "4781        0.719389 -0.209311   -2.420800   -1.382199        170.919739   \n",
       "6271        0.792039 -0.061905   -1.901546   -1.169657        144.637169   \n",
       "\n",
       "         recommendation  host_quanlity  \n",
       "host_id                                 \n",
       "5163         105.994201      42.153328  \n",
       "3534          97.390287      38.689000  \n",
       "4905          94.782252      37.645786  \n",
       "4781          85.003347      33.779749  \n",
       "6271          71.955307      28.537770  "
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "host.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ls=host.columns.tolist()\n",
    "mms = MinMaxScaler()\n",
    "for i in ls:\n",
    "    host[i]=mms.fit_transform(host[i].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>convenience</th>\n",
       "      <th>hotness</th>\n",
       "      <th>reputation</th>\n",
       "      <th>experience</th>\n",
       "      <th>cost_performance</th>\n",
       "      <th>recommendation</th>\n",
       "      <th>host_quanlity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5163</th>\n",
       "      <td>0.884534</td>\n",
       "      <td>0.006639</td>\n",
       "      <td>1.030777e-09</td>\n",
       "      <td>0.334868</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>0.992145</td>\n",
       "      <td>0.526753</td>\n",
       "      <td>3.951150e-01</td>\n",
       "      <td>0.637643</td>\n",
       "      <td>0.950956</td>\n",
       "      <td>0.957806</td>\n",
       "      <td>0.957538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4905</th>\n",
       "      <td>0.945838</td>\n",
       "      <td>0.047827</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.361125</td>\n",
       "      <td>0.944570</td>\n",
       "      <td>0.945016</td>\n",
       "      <td>0.944752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4781</th>\n",
       "      <td>0.966898</td>\n",
       "      <td>0.070838</td>\n",
       "      <td>1.632118e-06</td>\n",
       "      <td>0.383980</td>\n",
       "      <td>0.896321</td>\n",
       "      <td>0.897059</td>\n",
       "      <td>0.897367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6271</th>\n",
       "      <td>0.974190</td>\n",
       "      <td>0.090344</td>\n",
       "      <td>8.017537e-02</td>\n",
       "      <td>0.414436</td>\n",
       "      <td>0.832025</td>\n",
       "      <td>0.833070</td>\n",
       "      <td>0.833117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         convenience   hotness    reputation  experience  cost_performance  \\\n",
       "host_id                                                                      \n",
       "5163        0.884534  0.006639  1.030777e-09    0.334868          1.000000   \n",
       "3534        0.992145  0.526753  3.951150e-01    0.637643          0.950956   \n",
       "4905        0.945838  0.047827  0.000000e+00    0.361125          0.944570   \n",
       "4781        0.966898  0.070838  1.632118e-06    0.383980          0.896321   \n",
       "6271        0.974190  0.090344  8.017537e-02    0.414436          0.832025   \n",
       "\n",
       "         recommendation  host_quanlity  \n",
       "host_id                                 \n",
       "5163           1.000000       1.000000  \n",
       "3534           0.957806       0.957538  \n",
       "4905           0.945016       0.944752  \n",
       "4781           0.897059       0.897367  \n",
       "6271           0.833070       0.833117  "
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "host.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=host.head(10).index,y=\"host_quanlity\",kind=\"bar\",data=host.head(10),color=\"c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "new Promise(function(resolve, reject) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    script.onload = resolve;\n",
       "    script.onerror = reject;\n",
       "    script.src = \"https://assets.pyecharts.org/assets/echarts.min.js\";\n",
       "    document.head.appendChild(script);\n",
       "}).then(() => {\n",
       "\n",
       "});"
      ],
      "text/plain": [
       "<pyecharts.render.display.Javascript at 0x19b09a11f48>"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.globals import CurrentConfig, NotebookType\n",
    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Radar\n",
    "\n",
    "v1 = [host.iloc[0,:].values.tolist()]\n",
    "v2 = [host.iloc[1,:].values.tolist()]\n",
    "v3 = [host.iloc[2,:].values.tolist()]\n",
    "\n",
    "c = (\n",
    "    Radar()\n",
    "    .add_schema(\n",
    "        schema=[\n",
    "            opts.RadarIndicatorItem(name=\"convenience\", max_=host.convenience.max()),\n",
    "            opts.RadarIndicatorItem(name=\"hotness\", max_=host.hotness.max()),\n",
    "            opts.RadarIndicatorItem(name=\"reputation\", max_=host.reputation.max()),\n",
    "            opts.RadarIndicatorItem(name=\"experience\", max_=host.experience.max()),\n",
    "            opts.RadarIndicatorItem(name=\"cost_performance\", max_=host.cost_performance.max()),\n",
    "            opts.RadarIndicatorItem(name=\"recommendation\", max_=host.recommendation.max()),\n",
    "            opts.RadarIndicatorItem(name=\"host_quanlity\", max_=host.host_quanlity.max()),\n",
    "        ]\n",
    "    )\n",
    "    .add(\"host_quanlity1\", v1)\n",
    "    .add(\"host_quanlity2\", v2)\n",
    "    .add(\"host_quanlity3\", v3)\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        legend_opts=opts.LegendOpts(selected_mode=\"single\"),\n",
    "        title_opts=opts.TitleOpts(title=\"Radar-单例模式\"),\n",
    "    )\n",
    "\n",
    ")\n",
    "c.load_javascript()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"338a7ff465324d6992f5bc591fb1d23a\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_338a7ff465324d6992f5bc591fb1d23a = echarts.init(\n",
       "            document.getElementById('338a7ff465324d6992f5bc591fb1d23a'), 'white', {renderer: 'canvas'});\n",
       "        var option_338a7ff465324d6992f5bc591fb1d23a = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"host_quanlity1\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0.8845338672091457,\n",
       "                    0.006639100358986044,\n",
       "                    1.0307771858286685e-09,\n",
       "                    0.3348681462208396,\n",
       "                    1.0,\n",
       "                    1.0,\n",
       "                    1.0\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"host_quanlity2\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0.9921447191172822,\n",
       "                    0.52675269731227,\n",
       "                    0.3951150240004579,\n",
       "                    0.6376432622184784,\n",
       "                    0.9509564075857164,\n",
       "                    0.9578056323302422,\n",
       "                    0.9575384112106748\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"host_quanlity3\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0.9458382326380698,\n",
       "                    0.047827071716876224,\n",
       "                    0.0,\n",
       "                    0.3611247259770112,\n",
       "                    0.9445701717907139,\n",
       "                    0.9450155944037554,\n",
       "                    0.9447519416159442\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"host_quanlity1\",\n",
       "                \"host_quanlity2\",\n",
       "                \"host_quanlity3\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"host_quanlity1\": true,\n",
       "                \"host_quanlity2\": true,\n",
       "                \"host_quanlity3\": true\n",
       "            },\n",
       "            \"selectedMode\": \"single\",\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"radar\": {\n",
       "        \"indicator\": [\n",
       "            {\n",
       "                \"name\": \"convenience\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"hotness\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"reputation\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"experience\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"cost_performance\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"recommendation\",\n",
       "                \"max\": 1.0\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"host_quanlity\",\n",
       "                \"max\": 1.0\n",
       "            }\n",
       "        ],\n",
       "        \"name\": {\n",
       "            \"textStyle\": {}\n",
       "        },\n",
       "        \"splitLine\": {\n",
       "            \"show\": true,\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            }\n",
       "        },\n",
       "        \"splitArea\": {\n",
       "            \"show\": true,\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            }\n",
       "        },\n",
       "        \"axisLine\": {\n",
       "            \"show\": true,\n",
       "            \"onZero\": true,\n",
       "            \"onZeroAxisIndex\": 0\n",
       "        }\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Radar-\\u5355\\u4f8b\\u6a21\\u5f0f\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_338a7ff465324d6992f5bc591fb1d23a.setOption(option_338a7ff465324d6992f5bc591fb1d23a);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x19b0b6c0108>"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间序列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,[(ax1,ax2),(ax3,ax4)]=plt.subplots(2,2,figsize=(9,5))#绘制对称子图,子图大小\n",
    "sns.lineplot(x=\"last_review\", y=\"hotness\",ax=ax1,\n",
    "             data=newdf)\n",
    "sns.lineplot(x=\"year\", y=\"hotness\",ax=ax2,\n",
    "             data=newdf)\n",
    "sns.lineplot(x=\"month\", y=\"hotness\",ax=ax3,\n",
    "             data=newdf)\n",
    "sns.lineplot(x=\"weekday\", y=\"hotness\",ax=ax4,\n",
    "             data=newdf)\n",
    "plt.subplots_adjust(wspace=0.25,hspace=0.4) #调整子图间隙"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,[(ax1,ax2),(ax3,ax4)]=plt.subplots(2,2,figsize=(9,5))#绘制对称子图,子图大小\n",
    "sns.lineplot(x=\"last_review\", y=\"recommendation\",ax=ax1,\n",
    "             data=newdf)\n",
    "sns.lineplot(x=\"year\", y=\"recommendation\",ax=ax2,\n",
    "             data=newdf)\n",
    "sns.lineplot(x=\"month\", y=\"recommendation\",ax=ax3,\n",
    "             data=newdf)\n",
    "sns.lineplot(x=\"weekday\", y=\"recommendation\",ax=ax4,\n",
    "             data=newdf)\n",
    "plt.subplots_adjust(wspace=0.25,hspace=0.4) #调整子图间隙"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：短租的信息从2014年开始增长，18-19年猛增；而从月份来看3-4月份是旺季，而5月之后属于淡季;从周度来看，存在一定周末效应。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'name',\n",
       " 'host_id',\n",
       " 'host_name',\n",
       " 'neighbourhood',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'room_type',\n",
       " 'price',\n",
       " 'minimum_nights',\n",
       " 'number_of_reviews',\n",
       " 'last_review',\n",
       " 'reviews_per_month',\n",
       " 'calculated_host_listings_count',\n",
       " 'availability_365',\n",
       " 'year',\n",
       " 'month',\n",
       " 'weekday',\n",
       " 'is_workday',\n",
       " 'is_holiday',\n",
       " 'is_in_lieu',\n",
       " 'Polarity',\n",
       " 'ProbaPositive',\n",
       " 'room_type__count',\n",
       " 'neighbourhood__count',\n",
       " 'host_id__count',\n",
       " 'today',\n",
       " 'last_rewtonow',\n",
       " 'convenience',\n",
       " 'hotness',\n",
       " 'reputation',\n",
       " 'experience',\n",
       " 'cost_performance',\n",
       " 'recommendation',\n",
       " 'host_quanlity']"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19b640fac88>"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"is_workday\", y=\"hotness\", kind=\"bar\", data=newdf)\n",
    "sns.catplot(x=\"is_in_lieu\", y=\"hotness\", kind=\"bar\", data=newdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评论时间一般是在工作日"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 文本可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from stylecloud import gen_stylecloud\n",
    "from IPython.display import Image# 用于在jupyter lab中显示本地图片\n",
    "excludes = {\"我们\",\"特别\",\"可以\",\"入住\",\"非常\",\"真的\",\"但是\",\"还有\",\"没有\",\"问题\",\"一个\",\"就是\"}\n",
    "gen_stylecloud(file_path=\"Chinese_comments.csv\",#绘制词云的文本\n",
    "               font_path=r'C:\\Windows\\Fonts\\simhei.ttf',#字体路径\n",
    "               custom_stopwords=excludes, #停用词表\n",
    "#                palette=\"scientific.diverging.Broc_3\",# 设置配色方案\n",
    "               icon_name=\"fas fa-flag\",# 设置图标样式,使用库内自带的引用格式为：\"fas fa-图标标题\"\n",
    "               size=418,#控制输出图像文件的分辨率,默认512\n",
    "               max_font_size=75,# 设置最大字体\n",
    "               output_name=\"stylecloud.png\") #输出词云文件名\n",
    "\n",
    "Image(filename=\"stylecloud.png\") #调用本地图片在jupyter中显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_stylecloud(file_path=\"English_comments.csv\",#绘制词云的文本\n",
    "#                font_path=r'C:\\Windows\\Fonts\\simhei.ttf',#字体路径\n",
    "#                custom_stopwords=excludes, #停用词表\n",
    "#                palette=\"scientific.diverging.Broc_3\",# 设置配色方案\n",
    "               icon_name=\"fas fa-flag\",# 设置图标样式,使用库内自带的引用格式为：\"fas fa-图标标题\"\n",
    "               size=418,#控制输出图像文件的分辨率,默认512\n",
    "               max_font_size=75,# 设置最大字体\n",
    "               output_name=\"stylecloud-E.png\") #输出词云文件名\n",
    "\n",
    "Image(filename=\"stylecloud-E.png\") #调用本地图片在jupyter中显示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：正面评价很多，租客对于房源满意度较高。性价比、交通、房东性格、舒适度等比较受到重视。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 价格影响因素分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lgb_feat_selection(df,label,k=5):\n",
    "    clf = lgb.LGBMRegressor(objective='regression',metric= 'mae',silent=1,num_leaves=80,learning_rate=0.03, n_estimators=300)\n",
    "    clf.fit(df.drop(label,axis=1), df[label],categorical_feature=cat_feat,verbose=0) \n",
    "    keys=df.columns\n",
    "    values=clf.feature_importances_\n",
    "    feim=dict(zip(keys,values))\n",
    "    rank=sorted(feim.items(),  key=lambda d: d[1], reverse=True) #按得分降序\n",
    "    derank=sorted(feim.items(),  key=lambda d: d[1], reverse=False) #按得分升序\n",
    "    rank_keys=[i[0] for i in rank][0:k]\n",
    "    derank_keys=[i[0] for i in derank][0:k]\n",
    "    lgb.plot_importance(clf)\n",
    "    return {\"Kbest\":rank_keys,\"kworst\":derank_keys}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 25815 entries, 0 to 25814\n",
      "Data columns (total 34 columns):\n",
      " #   Column                          Non-Null Count  Dtype   \n",
      "---  ------                          --------------  -----   \n",
      " 0   id                              25815 non-null  int64   \n",
      " 1   name                            25815 non-null  category\n",
      " 2   host_id                         25815 non-null  category\n",
      " 3   host_name                       25815 non-null  category\n",
      " 4   neighbourhood                   25815 non-null  category\n",
      " 5   latitude                        25815 non-null  float16 \n",
      " 6   longitude                       25815 non-null  float16 \n",
      " 7   room_type                       25815 non-null  category\n",
      " 8   price                           25815 non-null  float64 \n",
      " 9   minimum_nights                  25815 non-null  float64 \n",
      " 10  number_of_reviews               25815 non-null  float64 \n",
      " 11  last_review                     15804 non-null  category\n",
      " 12  reviews_per_month               15804 non-null  float16 \n",
      " 13  calculated_host_listings_count  25815 non-null  float64 \n",
      " 14  availability_365                25815 non-null  float64 \n",
      " 15  year                            15804 non-null  float64 \n",
      " 16  month                           15804 non-null  float64 \n",
      " 17  weekday                         15804 non-null  float64 \n",
      " 18  is_workday                      15804 non-null  object  \n",
      " 19  is_holiday                      15804 non-null  object  \n",
      " 20  is_in_lieu                      15804 non-null  object  \n",
      " 21  Polarity                        15800 non-null  float64 \n",
      " 22  ProbaPositive                   15800 non-null  float64 \n",
      " 23  room_type__count                25815 non-null  float64 \n",
      " 24  neighbourhood__count            25815 non-null  float64 \n",
      " 25  host_id__count                  25815 non-null  float64 \n",
      " 26  today                           25815 non-null  object  \n",
      " 27  last_rewtonow                   25815 non-null  float64 \n",
      " 28  convenience                     25815 non-null  float64 \n",
      " 29  hotness                         15804 non-null  float64 \n",
      " 30  reputation                      15800 non-null  float64 \n",
      " 31  experience                      15800 non-null  float64 \n",
      " 32  cost_performance                15800 non-null  float64 \n",
      " 33  recommendation                  15800 non-null  float64 \n",
      "dtypes: category(6), float16(3), float64(20), int64(1), object(4)\n",
      "memory usage: 9.0+ MB\n"
     ]
    }
   ],
   "source": [
    "newdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_feat=['id',\n",
    " 'name',\n",
    " 'host_id',\n",
    " 'host_name',\n",
    " 'neighbourhood',\n",
    " 'room_type',\n",
    " 'is_workday',\n",
    " 'is_holiday',\n",
    " 'is_in_lieu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "for i in cat_feat:\n",
    "    newdf[i]=le.fit_transform(newdf[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf.drop(\"today\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_selector import FeatureSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector(data =newdf.drop(indicator+[\"price\"],axis=1), labels = newdf[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pass in the appropriate parameters\n",
    "fs.identify_zero_importance(task = 'regression',  eval_metric = 'mse', \n",
    " n_iterations = 50,  early_stopping = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>normalized_importance</th>\n",
       "      <th>cumulative_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>latitude</td>\n",
       "      <td>485.34</td>\n",
       "      <td>0.090939</td>\n",
       "      <td>0.090939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>host_name</td>\n",
       "      <td>468.60</td>\n",
       "      <td>0.087802</td>\n",
       "      <td>0.178741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>name</td>\n",
       "      <td>434.96</td>\n",
       "      <td>0.081499</td>\n",
       "      <td>0.260240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id</td>\n",
       "      <td>428.12</td>\n",
       "      <td>0.080217</td>\n",
       "      <td>0.340457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>longitude</td>\n",
       "      <td>398.86</td>\n",
       "      <td>0.074735</td>\n",
       "      <td>0.415192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>host_id</td>\n",
       "      <td>393.60</td>\n",
       "      <td>0.073749</td>\n",
       "      <td>0.488941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>reviews_per_month</td>\n",
       "      <td>355.04</td>\n",
       "      <td>0.066524</td>\n",
       "      <td>0.555466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ProbaPositive</td>\n",
       "      <td>337.98</td>\n",
       "      <td>0.063328</td>\n",
       "      <td>0.618793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>availability_365</td>\n",
       "      <td>294.78</td>\n",
       "      <td>0.055233</td>\n",
       "      <td>0.674027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>calculated_host_listings_count</td>\n",
       "      <td>229.56</td>\n",
       "      <td>0.043013</td>\n",
       "      <td>0.717040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          feature  importance  normalized_importance  \\\n",
       "0                        latitude      485.34               0.090939   \n",
       "1                       host_name      468.60               0.087802   \n",
       "2                            name      434.96               0.081499   \n",
       "3                              id      428.12               0.080217   \n",
       "4                       longitude      398.86               0.074735   \n",
       "5                         host_id      393.60               0.073749   \n",
       "6               reviews_per_month      355.04               0.066524   \n",
       "7                   ProbaPositive      337.98               0.063328   \n",
       "8                availability_365      294.78               0.055233   \n",
       "9  calculated_host_listings_count      229.56               0.043013   \n",
       "\n",
       "   cumulative_importance  \n",
       "0               0.090939  \n",
       "1               0.178741  \n",
       "2               0.260240  \n",
       "3               0.340457  \n",
       "4               0.415192  \n",
       "5               0.488941  \n",
       "6               0.555466  \n",
       "7               0.618793  \n",
       "8               0.674027  \n",
       "9               0.717040  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.feature_importances.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 features required for 0.90 of cumulative importance\n"
     ]
    }
   ],
   "source": [
    "# plot the feature importances\n",
    "fs.plot_feature_importances(threshold = 0.9, plot_n = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 26 original features\n",
      "There are 883 one-hot features\n"
     ]
    }
   ],
   "source": [
    "one_hot_features = fs.one_hot_features\n",
    "base_features = fs.base_features\n",
    "print('There are %d original features' % len(base_features))\n",
    "print('There are %d one-hot features' % len(one_hot_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 通过对房源信息进行量化，建立了'convenience','hotness', 'reputation', 'experience', 'cost_performance', 'recommendation'等指标，挖掘最受用户欢迎的房源，实现按需推荐。\n",
    "* 计算房东的质量分数，实现房东的精细化运营管理。\n",
    "* 时间序列分析表明：短租的信息从2014年开始增长，18-19年猛增；而从月份来看3-4月份是旺季，而5月之后属于淡季;从周度来看，存在一定周末效应。\n",
    "* 短租整体正面评价很多，租客对于房源满意度较高。性价比、交通、房东性格、舒适度等比较受到重视。"
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